Abstract
Objective: This study aims to investigate the structural and functional
characteristics of walnut protein hydrolysates (WPHs) with different
molecular weights prepared using protease from Dregea sinensis Hemsl,
as well as the anti-fatigue effects of low-molecular-weight walnut
protein hydrolysates (LWPs) and their impact on the cecal microbiota
and faecal metabolism of mice. Methods: The anti-fatigue activity of
WPHs with different molecular weights was evaluated, and the LWPs were
analyzed in a centralized manner. A 28-day gavage study was conducted
to assess LWP’s anti-fatigue benefits in mice, supplemented by
metabolomic analysis to explore its impact on metabolic pathways.
Results: Our findings revealed that LWP significantly outperformed
unhydrolyzed walnut protein (WP) in terms of water retention, lipid
retention, emulsifying properties, and foaming capacity. Notably,
differential protein expression associated with LWP highlighted
pathways related to antioxidant activity. In vivo studies showed that
LWP markedly enhanced glycogen storage in the muscles and liver of
mice, while reducing serum levels of serum urea nitrogen, lactate
dehydrogenase, blood lactic acid, and creatine kinase. Furthermore, the
levels of Superoxide Dismutase and Glutathione were significantly
elevated, alongside a reduction in Malondialdehyde, indicating that
LWP’s anti-fatigue effect is closely linked to improved oxidative
stress resistance. Additionally, LWP promoted beneficial increases in
microbial populations such as Akkermansia, Alistipes, Eubacterium, and
Muribaculum, which are associated with enhanced fatigue resistance.
Metabolomic analysis indicated significant enrichment in
glycerophospholipid metabolism and amino acid biosynthesis, identifying
key metabolites including palmitoylethanolamide and
4-methyl-5-thiazoleethanol, both of which are integral to health
maintenance. Conclusions: LWP demonstrates a robust anti-fatigue
effect, supported by its accessibility, straightforward preparation,
and eco-friendly characteristics. These attributes suggest that LWP has
promising potential for inclusion in health products aimed at enhancing
vitality and combating fatigue.
Keywords: walnut meal, Dregea sinensis Hemsl. Protease, cecum
microbiota, metabolomics
1. Introduction
Fatigue refers to the inability of an organism to maintain its
physiological functions at a certain level or the inability of the
organism to maintain its due exercise intensity [[42]1]. This can cause
transient muscle or organ hypofunction [[43]2], and usually includes
three kinds of neurological, motor, and psychological fatigue [[44]3].
If the fatigue state persists for more than 6 months, then it is called
chronic fatigue [[45]4]. The results of the survey conducted by the
World Health Organization (WHO) show that the number of people who are
in a fatigue state in the world accounts for more than 35% of the total
number of people, of which nearly 60% are middle-aged men [[46]5].
Along with the progress of modern society, the fast-paced life and
great pressure of life have made fatigue-related syndromes a serious
global health problem [[47]3]; in addition, fatigue is also a
complication of many diseases [[48]6,[49]7,[50]8,[51]9]. The
pathophysiology and aetiology of fatigue have not yet been fully
resolved, and the current types of medications for the relief of
fatigue-related symptoms are limited and have side effects
[[52]10,[53]11,[54]12]; therefore, it is important to seek for
functional foods with anti-fatigue effects instead of medications for
the relief of fatigue-related interventions. In recent years,
researchers have shown that natural active substances such as peptides,
polysaccharides, amino acids, vitamins, polyphenols, alkaloids, etc.,
have the efficacy of relieving physical fatigue
[[55]13,[56]14,[57]15,[58]16].
Walnuts are plants of the Juglans genus in the Juglandaceae family,
which is rich in nutrients such as omega-3 and omega-6 unsaturated
fatty acids, protein, fibre, vitamin E, B-complex, and minerals such as
zinc, potassium, calcium, magnesium, and iron. These have the effects
of reducing fat and benefitting energy levels, prolonging longevity,
moistening the lungs, strengthening the kidneys, nourishing the blood,
making hair healthy, and strengthening the brain, as well as preventing
coronary heart disease [[59]17,[60]18]. According to relevant studies,
walnut kernel contains rich bioactive components with important
functions such as antitumour [[61]19], antioxidant [[62]20],
anti-inflammatory, antimicrobial [[63]21], insecticidal activity
[[64]22], and analgesic [[65]23]. Walnuts are rich in protein, which is
a macronutrient that assumes many human physiological functions
[[66]24]. At present, the human body can obtain proteins that can be
utilised from animal, plant, and microbial sources [[67]25]. From a
nutritional standpoint, animal proteins have more nutrients than plant
proteins but plant proteins are easier to digest [[68]26], and from a
resource point of view, the production of plant proteins is more
economical and reasonable than animal proteins on the same area of land
[[69]27], which makes it clear that the study of plant proteins is of
great significance. In addition, relevant studies have shown that
adopting a plant-protein-based diet can reduce the risk of heart
disease [[70]28], diabetes, and certain cancers [[71]29]. Walnut
protein (WP) is a high-quality plant protein with economic, edible, and
medicinal values [[72]30], and its enzymatically produced bioactive
peptides have unique physiological functions, such as anti-ageing
[[73]31], antioxidant [[74]32], anti-radiation, fatigue elimination
[[75]33], and enhancement of the immune function of the human body.
Anti-fatigue peptides [[76]34] with easy absorption and few toxic side
effects are of great research significance in the fields of food and
medicine.
Dregea sinensis Hemsl. Protease, derived from the perennial climbing
woody vine Dregea sinensis, is a novel enzyme system with significant
industrial potential. Traditionally, aqueous extracts from its stems
have been utilized as a natural coagulant in Yunnan, China, for over
six centuries to produce milk pastry (a traditional milk cake),
replacing conventional acid-based curdling agents. Recent biochemical
characterization reveals that this protease complex predominantly
consists of a cysteine protease, procerain B, with a molecular weight
of 23.8 kDa, exhibiting exceptional thermostability and pH tolerance
[[77]35]. These properties underscore its dual role as a sustainable
alternative in dairy processing and a promising biocatalyst for
functional food development.
In this study, we aimed to investigate whether low-molecular-weight
walnut protein hydrolysates (LWPs), prepared through enzymatic
digestion of walnut proteins using Dregea sinensis Hemsl. protease, can
alleviate exercise-induced fatigue in mice. Specifically, we explored
the effects of LWP on fatigue using a weight-bearing exhaustion
swimming test, while also examining its impact on the intestinal
microbiota and metabolic profiles of mice. The findings of this study
are intended to provide scientific evidence for the development of
functional foods targeting fatigue management as an adjunct to clinical
practice.
2. Materials and Methods
2.1. Materials and Reagents
Walnut protein was extracted from walnut meal and industrially
produced. The Dregea sinensis Hemsl was harvested from the Kunming
hills by experienced farmers, and we extracted the Dregea sinensis
Hemsl Protease by graded chromatography. Blood urea nitrogen (BUN),
lactate (LD), creatine kinase (CK), hepatic glycogen, and myo-glycogen
assay kits were purchased from Nanjing Jianjian Institute of
Biotechnology (Nanjing, China). Bicinchoninic acid (BCA) protein
concentration kit, ECL reagent, and methanol were purchased from
Biyuntian Institute of Biotechnology (Shanghai, China); C[6]H[8]O[7],
NaOH, HCl, and (NH[4])[2]SO[4] from Chengdu Jinshan Chemical Reagent
Co., Ltd. (Chengdu, China).
2.2. Preparation of WPHs
Referring to the method of Jin [[78]36] with modification, walnut
protein was mixed with ultrapure water at a material–liquid ratio of
1:10 and then placed at 60 °C for 12 min of sonication. The pH was
adjusted to 8.6 using 1 mol/L NaOH and Dregea sinensis Hemsl. Protease
with a substrate concentration of 3% was added, and the enzymatic
hydrolysis was carried out for 4.5 h in a constant-temperature water
bath. Following, the enzymes were inactivated for 10 min at 90 °C after
the end of the enzymatic hydrolysis, and then lowered to room
temperature and centrifuged for 15 min in a low-temperature centrifuge
at 4000 r/min. Then, it was reduced to room temperature, and the
hydrolysate obtained was centrifuged in a low-temperature centrifuge at
4000 r/min for 15 min. The supernatant, i.e., WPHs, was taken, and a
portion of it was removed and lyophilised for spare use, the remaining
was subjected to ultrafiltration.
2.3. Ultrafiltration Crude Separation
The WPHs were purified by the ultrafiltration membrane method to
isolate walnut proteolytic proteins with molecular weights less than
3000 Da (LWPs), walnut proteolytic proteins with molecular weights
between 3000 and 5000 Da (MWPs), and walnut proteolytic proteins with
molecular weights greater than 5000 Da (HWPs).
2.4. Functional Characterisation of WPHs
A total of 200 mg of sample (m[1]) was added to the centrifuge tube and
the weight of the tube was recorded (m[0]). Then, 5 mL of water or corn
oil was added to the tube, vortexed continuously for 1.5 h. The tube
was centrifuged at 5000× g for 10 min and the supernatant was removed
and weighed (m[2]). The water-holding capacity (WHC) and oil-holding
capacity (OHC) were calculated as follows:
[MATH:
WHC(g/g)=m2−
m1−m0m1
:MATH]
[MATH:
OCH(g/g)=m2−
m1−m0m1
:MATH]
The samples were weighed in 100 mL of distilled water, prepared into
neutral protein peptide solutions of different concentrations, and the
initial foam volume (V[0]) was recorded after 10 min at different
temperatures. Homogenisation was performed in a high-speed tissue
masher at 10,000 r/min for 3 min, the foam volume (V[1]) was accurately
recorded at the time of stopping of homogenisation, and the foaming
stability of the samples was calculated according to the following
formula (FC). The foam volume (V[2]) at the end of homogenisation for
30 min was recorded at room temperature, and the foaming stability (FS)
of the samples was calculated according to the formula. Each sample was
measured 3 times:
[MATH: FC=V1100×100%
:MATH]
[MATH: FS=V2V0×<
/mo>100% :MATH]
The emulsifying ability index (EAI) and emulsion stability index (ESI)
of the samples were determined by dissolving the samples in PBS
solution (10 mM, pH 7.2) to form a 10 mg/mL dispersion. Then, 15 mL of
the peptide solution was mixed with 5 mL of corn oil and homogenised
for 2 min. The homogenised emulsion (0.5 mL) was diluted with 50 mL of
0.1% SDS and the absorbance was measured at 500 nm (A[0]). The diluted
emulsion was detected under the same conditions after 10 min and was
recorded as A[1]. The 0.1% SDS absorbance was used as a blank control.
The results were calculated by the following formula:
[MATH:
EAI(<
mi mathvariant="normal">m2/g)=2×2.303
×A0×DFC×∅×θ×
10000 :MATH]
[MATH: ESI=A0×
10ΔA :MATH]
where DF is the dilution factor; C is the protein concentration; and
the oil volume fraction, 0.01 m [optical range], is the difference
between A[0] and A[1].
2.5. Scanning Electron Microscope Analysis (SEM)
A total of 1 mg of each sample powder was taken and uniformly coated on
a specimen holder adhered with double-sided conductive tape, sprayed
with gold coating treatment for 5 min to ensure that the samples were
encapsulated by gold, and then observed and photographed using a
scanning electron microscope, for which the accelerating voltage was
15.0 kV, the working distance was 12.8 mm, the magnification was
20,000×, and the working temperature was 25 °C.
2.6. X-Ray Diffraction (XRD)
A total of 10 mg of each sample powder was taken and ground uniformly.
Then, the samples were loaded into a sample plate, placed in a holder,
and X-ray diffraction data were collected using an XRD diffractometer
(Shanghai Aiyitong Network Technology Co., Ltd., Shanghai, China).
2.7. Fourier-Transform Infrared Spectroscopic Analysis (FTIR)
In total, 1 mg of each sample powder was taken and mixed with 100 mg of
dried KBr, uniformly ground to less than 2.5 μm, and pressed using the
KBr pressing method. The samples were scanned using FTIR over a
scanning range of 4000 to 500 cm^−1, with 16 sweeps, and a spectral
resolution of 4 cm^−1, therefore, the absorption spectra were recorded.
2.8. Peptide Identification
Based on the preliminary pre-tests, it was shown that the anti-fatigue
activity of LWPs with a molecular weight less than 1000 Da was slightly
better than that of LWPs with a molecular weight less than 3000 Da.
Since we are targeting health food products, it is more economical to
choose an LWP with a molecular weight of less than 3000 Da, which can
greatly reduce the R&D cost given the small difference in anti-fatigue
activity. LWP samples with 0.2 μg molecular weight less than 3000 Da
were taken and separated by a nano-UPLC liquid-phase system and then
coupled to a mass spectrometer equipped with a nanolitre ion source for
data acquisition. The chromatographic separation was performed on a 75
μm ID × 15 cm reversed-phase column, and the mobile phases were
acetonitrile–water–formic acid, with 0.1% formic acid–water in mobile
phase A and 0.1% formic acid–acetonitrile in mobile phase B. The raw
data were collected using a SpectroMicroSystems^® liquid-phase system.
The raw data files were searched using SpectroMine software (Tencent
cloud, [79]https://biognosys.com/software/spectromine/ (accessed on 7
March 2025)) with the Pulsar search engine, and qualitative analyses
were carried out at the end of the search.
2.9. Animal Experimental Design
All animals were kept in accordance with the guidelines for
experimental animals, and the animal experiments were approved by the
Institutional Animal Care and Utilisation Committee of Yunnan
Agricultural University. Eighty 6-week-old male ICR mice of 18–22 g
were selected and kept in a clean room for experimental animals with
cool ventilation, 50–60% relative humidity, and a room temperature of
(25 ± 2) °C, and the mice were free to eat and drink during the feeding
period. After one week of adaptive feeding, the mice were screened for
swimming, and mice that could not swim or had uncoordinated swimming
postures were excluded. Seventy-two mice were finally selected. The
eligible mice were randomly divided into 6 groups: group A—quiet
control group (q-con); group B—exercise model group (e-con); group
C—positive vitamin C control group (100 mg/kg PV); group D—walnut
proteins peptide low-dose group (50 mg/kg LWP-L); group E—walnut
proteins peptide medium-dose group (100 mg/kg LWP-M); Group F—high-dose
group of walnut protein peptide (200 mg/kg LWP-H). Dose selection was
based on pre-experimentation. Twelve mice in each group—the control and
model groups—were gavaged with the corresponding dose of saline every
day, the experimental cycle was 28 d, and the mice in each group were
weighed every day.
After the last gavage for 30 min, six mice were randomly removed from
each group. The mice were given 10% of their body weight in lead, and
placed in a swimming box at a water depth of 40 cm and a water
temperature of 25 °C. The time from the start of swimming to the time
when the head was completely submerged in the water and could not rise
to the surface for 8 s was recorded as the time of the weight-bearing
swimming of the mice. The remaining six mice in each group were
dissected.
2.10. Biochemical Analysis of Serum and Liver, Muscle
Biochemical analyses of serum and liver and muscle samples were
performed, including hepatic glycogen (HG), myo-glycogen (MG), serum
urea nitrogen (BUN), blood lactate (LA), creatine kinase (CK), and
lactate dehydrogenase (LDH). Muscle tissues were fixed with 4%
paraformaldehyde, embedded in paraffin, sectioned and stained with
hematoxylin and eosin (H&E), followed by light microscopy for routine
morphological analysis.
2.11. Gut Microbiota Analysis of Cecum Contents
Bacterial DNA was isolated from cecum contents and prepared for the 16S
rRNA gene sequencing analysis. The 16S rRNA gene was subjected to
sequencing of the V3-V4 region and data processing, and the 16S rRNA
gene was amplified with forward primer (50-CCTACGGRRBGCASCAGK
VRVGAAT-30) and reverse primer (50-GGACTACNVGGGTWTCTAATCC-30) to
amplify the V3-V4 region of the 16S rRNA gene.
2.12. Faecal Metabolomics Analysis
Sample handling methods, NMR data acquisition, and processing were
performed using previously described methods. Raw data were
preprocessed using MestReNova 10.0 and multivariate analyses were
performed using SIMCA 14.0 software (Umetrics, ume, Sweden), including
principal component analysis (PCA) and orthogonal partial least-squares
discriminant analysis (OPLS-DA). In OPLS-DA, potential biomarkers that
contributed significantly to group separation were identified based on
variable importance projection (VIP) values > 2 and p < 0.05.
Metabolomics pathway analysis (MetPA) was performed using MetaboAnalyst
(McGill University, Montréal, QC, Canada,
[80]https://www.metaboanalyst.ca/).
2.13. Statistical Analyses
All data are expressed as mean ± SD, the statistical differences were
analysed by ANOVA and Tukey’s test, and the normality of the data was
tested prior to ANOVA and post hoc tests. Data were analysed using SPSS
24.0 (SPSS Inc., Chicago, IL, USA), GraphPad Prism version 5.01
(GraphPad software, San Diego, CA, USA), and correlations were analysed
by Spearman’s correlation. Heatmaps were created using the Heml package
(Heatmap Illustrator, version 1.0). p-values < 0.05 were considered
statistically significant differences.
3. Results
3.1. Functional Properties of WPHs
Water-holding capacity is the ability to capture and immobilise water
after the formation of water-based colloids under external forces and
is an important property of proteins in the food industry [[81]37].
[82]Figure 1A shows that the water-holding capacity of walnut proteins
was significantly increased by enzymatic hydrolysis, which may be
related to the high solubility of the enzymatic products. The WPHs were
more easily dispersed in water, which led to a decrease in the free
water and an increase in the bound water, which resulted in an increase
in the water-holding capacity—with the best water-holding capacity of
the LWPs—which may be due to the fact that their small-molecule peptide
chains contain more hydrophilic groups. The water-holding capacity of
the WPHs was the lowest at a pH of 4, and with an increase in pH the
water-holding, capacity increased. The water-holding property increased
probably due to the fact that WPHs exist in ionic form away from the
isoelectric point, which enhances the solubility and viscosity of the
protein [[83]38]. Oil-holding capacity affects the organoleptic quality
of protein products and their physical properties during food
processing and storage. [84]Figure 1B shows that the oil-holding
capacity of WP was significantly increased by enzymatic digestion and
continued to increase over the range of measured temperatures, probably
due to the increase in temperature that increased the lipophilic
binding sites in the side chains of amino acid residues of WPHs. The
oil-holding capacity of the WPHs stabilised at 65 °C and reached a
maximum value of 2.39 g/g at 85 °C. Emulsification refers to the
ability of proteins to form emulsions at the oil–water interface,
whereas the emulsification stability measures the ability of the
emulsion to maintain its structure and state [[85]39]. [86]Figure 1C
shows that the enzymatic treatment enhanced the emulsifying ability of
WPHs, probably due to their increased solubility, which promoted the
migration to form a cohesive film at the water–oil interface and
improved the emulsibility. A pH of 4 showed the lowest emulsifiability
and emulsion stability of the WP and WPHs, and deviation from the
isoelectric point resulted in the enhanced solubility and dispersion of
proteins, which promoted the stability of the oil–water interface.
Foaming properties reflect the ability of proteins to migrate to the
air–water interface and form a rigid film [[87]40]. [88]Figure 1D shows
that the foaming property and foam stability of WP were enhanced at
different pH values after enzymatic digestion, probably due to the
stretching of the peptide chains of WPHs that reduced the surface
tension of the liquid bubbles and enhanced the bubble stability. A pH
of 4 showed the lowest foaming property and foam stability of the WP
and WPHs, which increased gradually when deviating from the isoelectric
point and reached the maximum value at pH 8. In addition, the foaming
property and foam stability of HWP were better than those of LWP and
MWP.
Figure 1.
[89]Figure 1
[90]Open in a new tab
(A) Water-holding capacity, (B) oil-holding capacity, (C)
emulsification stability, and (D) foaming stability of WP and WPHs of
various molecular weights.
3.2. SEM, XRD, and FTIR Analyses
In order to explore the surface microstructure of each molecular
segment of walnut protein digests, scanning electron microscopy was
used. As shown in [91]Figure 2A, the surface of low-molecular-weight
walnut peptide (LWP) appears smooth. In contrast, the microscopic
surface of medium-molecular-weight walnut peptide (MWP), shown in
[92]Figure 2B, shows a fish-scale-like structure and a less-smooth
surface than that of LWP. [93]Figure 2C demonstrates
high-molecular-weight walnut peptide (HWP), which has a lamellar or
blocky surface and a large, more porous, and sparse surface. These
observations suggest that there are significant differences in the
surface microstructure of walnut peptides with different molecular
weights, and that the surface of walnut peptides with smaller molecular
weights is relatively smoother.
Figure 2.
[94]Figure 2
[95]Open in a new tab
(A) SEM of LWP, (B) SEM of MWP, (C) SEM of HWP, (D) XRD plots of WPHs
with different molecular weights, and (E) FTIR plots of WPHs with
different molecular weights.
X-ray diffraction is a good analytical method to study the
microstructure of substances by crystallographic changes. From the
diffraction patterns of MWP and HWP shown in [96]Figure 2D, a major
weakly dispersed diffraction peak is observed near 2θ of 20°, while LWP
has a weakly dispersed diffraction peak near 2θ of 22.4°, which
suggests that LWP, MWP, and HWP are not in an ordered arrangement but
are randomly amorphous structures. All three have diffraction peaks
near 2θ of 20°, presumably due to the α-helical structure in LWP, MWP,
and HWP.
IR spectra are usually used to detect the formation and composition of
organic functional groups, mainly including O-H, C-O, and N-H. The
bands between 4000 cm^−1 and 500 cm^−1 were selected and deconvolved
after baseline correction. As can be seen in [97]Figure 2E, there is a
high-frequency absorption peak at around 3300 cm^−1 for LWP, MWP, and
HWP, which is caused by the O-H and N-H stretching vibrations, while
2929 cm^−1, 2951 cm^−1, and 2848 cm^−1 correspond to the C-H stretching
vibrations. In addition, amide I band vibrations caused by C=C
stretching were observed at 1654^−1, 1656^−1, and 1665^−1, and amide
III band vibrations caused by C-H bending at 1359^−1, 1397^−1, and
1395^−1. The presence of similar bands for different molecular weights
of walnut protein digests indicates the presence of the same major
functional groups for LWP, MWP, and HWP.
3.3. Identification and Screening of LWP
Ultrafiltration can intercept large molecules and effectively separate
and enrich low-molecular-mass peptides with high biological activity
[[98]41], which can also reveal the mechanism of LWP activity at the
molecular structure level [[99]42]. In this study, WPHs with different
molecular masses were prepared by ultrafiltration separation. In our
anti-fatigue pre-tests, WPHs with molecular weights less than 3000 Da
were found to have significant anti-fatigue effects. Therefore, we
identified the amino acid sequences of LWPs with molecular weights less
than 3000 Da by LC-MS/MS, and 2927 peptide sequences were identified
from LWPs by uniprot database comparison analysis. As shown in
[100]Figure 3A,B, these peptides mainly consisted of 7−20 amino acids,
and were dominated by short peptides with molecular masses less than
1500 Da (61%).
Figure 3.
[101]Figure 3
[102]Open in a new tab
(A) Molecular weight distribution of peptides; (B) chain length peptide
identification of peptides; (C) GO functional annotation.
The differentially expressed LWPs were subjected to GO functional
annotation, [103]Figure 3C, to determine the biological functions they
mainly exercise, and a total of 30 significant GO entries were
annotated for the differentially expressed proteins. Among them, in the
cell component, differentially expressed LWP was mainly involved in the
cell (GO:0005623), cell part (GO:0044464), intracellular (GO:0005622),
and cytoplasm (GO:0005737); in molecular function, differentially
expressed LWP was mainly localised in oxidoreductase activity
(GO:0016491), structural molecule activity (GO:0005198), and unfolded
protein binding (GO:0051082), etc.; in biology processes, differential
expression of LWP in the organonitrogen compound metabolic process
(GO:1901564), small molecule metabolic process (GO:0044281), and
organic acid metabolic process (GO:0006082) were significantly
enriched. Thus, the main differential expression of LWP was in
oxidoreductase activity and molecular metabolism, and had significant
effects on cellular and intracellular components. KEGG is an
informational network connecting known molecules to each other, and the
top 10 most significantly enriched classified pathways, [104]Figure 4B,
shows that the differentially expressed proteins were annotated to 32
KEGG signalling pathways in the KEGG database, which were mainly
involved in the carbon metabolism pathway, metabolic pathways pathway,
biosynthesis of secondary metabolites signalling pathway,
glycolysis/gluconeogenesis pathway, protein processing in the
endoplasmic reticulum pathway, and the ribosome pathway. The results of
database comparisons, [105]Figure 4C, show posttranslational
modification, protein turnover, chaperones, carbohydrate transport and
metabolism, energy production and conversion, amino acid transport and
metabolism, and signal transduction mechanisms. Among them, the most
frequently occurring proteins were cellular process and signal
transduction proteins, and the least frequently occurring proteins were
information storage and processing proteins; the function of one
protein was undetermined. Subcellular structure annotation of LWP
differentially expressed proteins was performed using PSORTb software
([106]https://www.psort.org/psortb/ (accessed on 7 March 2025)), and
the results in [107]Figure 4A show that 466 differentially expressed
proteins were localised to 17 entries, which were 188 cytoplasm, 54
secreted, 58 nucleus, 47 mitochondrion, 41 chloroplast, 12 peroxisome,
11 vacuole, 11 plasma membrane, 10 mitochondrion membrane, 9
endoplasmic reticulum membrane, 9 endoplasmic reticulum, 4 Golgi
apparatus membrane, 4 Golgi apparatus, 3 chloroplast membrane, 3
vacuole membrane, 1 peroxisome membrane, and 1 plastid. Taken together,
the identification results all indicate that LWP possesses antioxidant
function, which echoes the results of antioxidant indices in animal
experiments.
Figure 4.
[108]Figure 4
[109]Open in a new tab
(A) Subcellular localisation; (B) KEGG analysis; (C) COG functional
classification.
3.4. LWP Reduces Fatigue Induced by Strenuous Exercise
Muscle damage can be visualised by looking at the morphology of the
muscle tissue. [110]Figure 5A shows the results of the HE staining of
muscle fibres. As shown in the figure, the skeletal muscle cells of the
quiet control group and the exercise control group were complete and
neatly arranged; the fibroblasts of the positive control group with
gavage of Vc were neatly arranged and full, with complete morphology
and hypertrophied myofibres. Myofibroblasts in the walnut protein
peptide group were morphologically complete and myofibrils were
hypertrophied. The number of myosatellite cells was significantly
reduced compared with the two control groups. This suggests that the
walnut protein peptide intervention has a protective effect on skeletal
muscle, reduces the damage to skeletal muscle cells caused by exercise,
and enhances the exercise capacity of mice.
Figure 5.
[111]Figure 5
[112]Open in a new tab
(A) Fatigue indicators; (B) muscle morphological analysis of mice in
each group at 20× magnification.
As shown in [113]Figure 5B, LWP prolonged the weight-bearing swimming
time of mice by 6.36–28.91% compared with the exercise control group.
In terms of glycogen consumption, the hepatic glycogen (HG) content of
mice in the LWP-H group was 15.39–30.08% higher than that of the
exercise control group, and it also significantly reduced the
consumption of myo-glycogen (MG) in mice; moreover, LWP cleared the
accumulation of lactic acid (LA) and serum urea nitrogen (BUN) in the
blood of the mice (p < 0.05), and the LWP group’s blood LA and BUN
levels were reduced by 17.42–25.92% and 4.8–16.61%, respectively,
compared with those of the exercise control group. LWP was able to
significantly (p < 0.05) reduce serum creatine kinase (CK) and lactate
dehydrogenase (LDH) activities in rats, which resulted in the reduction
in CK’s viability in serum by 21.04–31.23% and LDH’s viability in serum
by 20.27–35.23% compared with that of the exercise control group. LWP
was also capable of reducing the accumulation of urea nitrogen (BUN) in
the blood of mice in the LWP-H group, which decreased by 35.35%;
therefore, our extracted LWP has better anti-fatigue efficacy. LWP can
effectively increase the activity of antioxidant enzymes GSH and SOD in
the serum of mice (p < 0.05), and decrease the content of MDA in serum
(p < 0.05) so we judge that the anti-fatigue process may be related to
the enhancement of its oxidative stress capacity. In the identification
of LWP, this molecular weight peptide was found to have a significant
antioxidant signalling pathway, and the results were consistent with
oxidative indicators.
3.5. Effect of LWP on Intestinal Flora of High-Intensity Exercise Mice
The feature sequences obtained after quality control and denoising are
represented by Venn diagrams showing the number of unique ASVs and
shared ASVs in each group. As shown in [114]Figure 6A, the e-CON,
q-CON, VC, LWP-L, LWP-M, and LWP-H groups contained 2174, 2114, 1703,
1780, 2623, and 1710 ASVs, respectively, and the number of total ASVs
in the six groups was 498. pCoA analysis showed that there was a
significant difference between the LWP-H group and the q-con, with
70.98% for PC1 and 7.8% for PC2, see [115]Figure 6B. The dilution
curves indicated that the amount of sequencing data could reflect the
species diversity of the samples, and the curves all tended to be flat,
which could cover most of the samples to a certain extent, as seen in
[116]Figure 6C. α diversity is an indicator of species richness,
diversity, and homogeneity, and is also known as diversity within
habitats. Studies have shown that the consumption of certain functional
foods or ingredients can modulate the composition and diversity of the
intestinal microflora, and whether LWP—as a bioactive ingredient from
walnut proteins—may also exert certain effects by modulating intestinal
dysbiosis in C57 mice, for which intestinal contents of mice were
collected for 16S rRNA gene sequencing. The results of the α-diversity
analysis of the intestinal flora, shown in [117]Figure 6D,E, displays
that the diversity indices of the intestinal flora of mice in the LWP-L
group, Simpson’s index, and Shannon’s index were significantly higher
than those of the other five groups, which indicated that the number of
microbial community species and homogeneity were high in the LWP-L
group. Compared with the blank group, the abundance, coverage,
homogeneity, and diversity of the walnut peptide group changed—in which
the diversity index changed significantly, indicating that the
structural composition of the intestinal flora changed. Among the three
experimental groups, the LWP group had the highest abundance and
diversity indices, which were significantly higher than those of the
LWP-M and LWP-H groups, suggesting that LWP altered the structural
composition of the intestinal microbiota of mice but more significantly
in the low-dose group.
Figure 6.
[118]Figure 6
[119]Open in a new tab
(A) Wayne plot of ASVs (Groups A–F are subgroups for animal
experiments, as described in 2.9); (B) PCoA plot; (C) dilution curve;
(D) Simpson’s exponent; (E) Shannon’s exponent. * p ≤ 0.05, ** p ≤
0.01.
3.6. Structural Composition of Microbial Community
Based on the results of the assay, the relative abundance of bacteria
at the phylum and genus levels was plotted. As shown in [120]Figure
7A,B, at the phylum level, the most prevalent bacteria in each group
were Thick-walled bacteria, Warble microbacteria, Bacteroidetes, and
Desulfovibrio. At the genus level, Thick-walled bacteria, Warty
microbacteria, Bacillus mimics, and Desulfurised bacteria were
prevalent in the six taxa. At the phylum level, relative to q-con, an
increase from 40.26% to 44.13% was observed in the Thick-walled phylum
of PV, an increase of 8.32% in the Thick-walled phylum, and a decrease
of 20% in the Warty microflora in the LWP-M group, and there was no
dose-dependence between the alteration of the bacteria at the phylum
level and the subjects. Whereas the LWP-H group had an increase of
19.06% in the Wart microflora gates, the Anamorphic bacillus gates were
almost the same as the rest of the groups, whereas the LWP-M group had
an increase of 10%. The number of Desulfovibrio spp. in the three
experimental groups did not differ much between the control and
positive groups. At the genus level, the genus Micrococcus wartyi was
the most abundant in LWP-L and LWP-H compared to q-con, with an
increase in abundance in both experimental groups (5.99 per cent in
LWP-L and 19.06 per cent in LWP-H). The relative abundance of
Muribaculaceae was highest in the LWP-M group, and decreased by 0.02
per cent in the LWP-L group and by 1.5%.
Figure 7.
[121]Figure 7
[122]Open in a new tab
(A) Relative abundance of bacteria at the phylum level; (B) relative
abundance of bacteria at the genus level. (Groups A–F are subgroups for
animal experiments, as described in 2.9).
In the microecological study, we also compared the association analyses
between species and environmental factors. The correlation between
species was calculated by the abundance and change relationship of
different species in each sample, and the species that were correlated
with each other were found by filtering under certain conditions. We
defaulted to show the relationship pairs with correlation coefficient
|rho| > 0.8, as shown in [123]Figure 8A, the gut microbial communities
of mice gavaged with LWP all formed a unique microbial network
containing 12 nodes and 11 edges, with a positive correlation of 72.73%
and a negative correlation of 27.27%, and the nodes of their network
mainly belonged to the phylum Thick-walled Bacteria and the warty
microbial phylum, which accounted for 90.9% of all the nodes and were
the dominant phylum of the bacterial community. To further investigate
the effect of genus level LWP on the level of enteric bacteria, we
plotted a heatmap at the genus level, and as can be seen in [124]Figure
8B, the remaining groups showed changes in other strains with lower
abundance compared to q-con, and all of them showed a positive
correlation with AKK bacteria. In the e-con group, strain abundance
increased in Clostridium and Bacteroides and decreased in Anaerotignum.
In the LWP-M group, the increase was in Alistipes, Eubacterium siraeum,
and Muribaculum.
Figure 8.
[125]Figure 8
[126]Open in a new tab
(A) Correlation network diagram of mouse gut microbial communities; (B)
OTU-based heatmap of correlation of mouse gut flora.
3.7. GC-MS Analysis of Faecal Metabolites
As shown in [127]Figure 9A, a total of 1256 qualitatively different
metabolites were observed in group A and group B. Among them, 598
substances were up-regulated, and the top five metabolites with larger
VIP values were PE (18:3(9Z,12Z,15Z)/14:0), Medicagenic acid, Setariol,
and 20-Hydroxy-PGE2, in that order; a total of 658 substances were
down-regulated, and the top five metabolites with larger VIP values
were Shikimic acid, 2,6-Dimethylaniline, Thiamine, Styrene, and LysoPE
(18:2(9Z,12Z)0:0), in that order. The q-con group compared with the PV
group had a total of 1829 qualitatively different metabolites, of which
1107 were up-regulated, and the top five metabolites with larger VIP
values were, in order, LysoPA (0:0/18:2(9Z,12Z),
(23S,24S)-17,23-Epoxy-24,29-dihydroxy-27-norlanost-8-ene-3,15-dione,
Benzoylmesaconine, Cucurbitacin IIb, and 4-Acetoxyscirpene-3,15-dio. A
total of 722 substances were down-regulated in total, and the top five
metabolites with higher VIP values were, in order, Heptadecanoyl
carnitine, Oxytetracycline, Dethiobiotin, 2,6-Dimethylaniline, and
(-)-Epigallocatechin. The total number of qualitatively different
metabolites in group A and group E was 2112, of which 1058 were
up-regulated, and the top five metabolites with larger VIP values were
Palmitoylethanolamide, 4-Methyl-5-(-)-thiazoleethanol, and
(-)-Epigallocatechin, in that order. Other metabolites include
thiazoleethanol, Phenacetin, 2,3-Dinor-6-keto-prostaglandin F1 a,
2,4,6-(2,4,6-) trihydroxybenzoic acid, and 2,4,6-(2,4,6-)
trihydroxybenzoic acid. A total of 1054 substances were down-regulated,
and the top five metabolites with large VIP values were
N-Acetylhistamine, Oxytetracycline, Etifoxine, Histamine, and MOPS, in
that order.
Figure 9.
[128]Figure 9
[129]Open in a new tab
(A) Volcanic map of differential metabolites; (B) K-means clustering
analysis of the differentially accumulated metabolites into nine
clusters according to their expression profile. The cluster names and
the number of metabolites for each cluster are indicated. (The masked
metabolite in A vs. C is (23S,
24S)-17,23-Epoxy-24,29-dihydroxy-27-norlanost-8-ene-3,15-dione; A vs. F
is Combretastatin A4).
To identify metabolites with consistent expression patterns in the
clusters, we used the K-means clustering algorithm to group metabolites
based on the similarity of metabolome profiles in [130]Figure 9B. A
total of nine clusters were identified, which could be divided into six
categories: both A vs. C and D vs. E were up-regulated (categories 1
and 4); A vs. C was up-regulated but D vs. E was down-regulated
(categories 2 and 7); A vs. C was up-regulated but D vs. E was
unchanged (categories 3 and 5); both A vs. C and D vs. E were unchanged
(category 6); both A vs. C and D vs. E were down-regulated (category
8); and A vs. C was down-regulated but D vs. E was unchanged (category
9). In addition, in the clusters with a general downward trend in
metabolite accumulation, cluster 8 metabolite assembly was enhanced in
A and C but decreased in D and E.
In this study, metabolomics analyses of mouse faecal samples from
different groups were carried out, and the QC-TIC plots were
superimposed on the multi-peak detection plots of the samples
[131]Figure 1, which demonstrated that the metabolomics data measured
in the present study had good reproducibility and reliability. A total
of 619 metabolites were identified, of which 159 (25.687%) were lipids
and lipid-like molecules, 103 (16.64%) were organic acids and their
derivatives, 55 (8.885%) were organic heterocyclic compounds, 47
(7.593%) were fatty acids (FA), 41 (6.624%) were amino acids and
peptides, 39 (6.3%) were alkaloids, and benzene compounds had 31
species (5.008%), mangiferates and phenylpropanoates 30 species
(4.847%), organic oxides 25 species (4.039%), carbohydrates 24 species
(3.877%), phenylacetones and polyketides 17 species (2.746%), organic
nitrogen compounds 11 species (1.777%), terpenoids 11 species (1.777%),
nucleosides, nucleotides, and analogues 6 (0.969%), Alkaloids and
derivatives 3 (0.485%), polyketides 3 (0.485%), lignans, neolignans,
and related compounds 1 (0.162%), and organosulfur compounds 1 (0.162%)
([132]Figure 10A). As shown in the high-level Wayne diagram, faecal
metabolites of mice gavaged with different samples were both common and
unique. The metabolic sets and pathways of 108 differential metabolites
were analysed, and the results are shown in [133]Figure 10B, where LWP
affected 15 metabolic sets and no significant enrichment was observed.
In [134]Figure 10C, LWP affected fifteen pathways, of which four were
significantly enriched: choline metabolism in cancer,
glycerophospholipid metabolism, sphingolipid metabolism, and the
biosynthesis of amino acids.
Figure 10.
[135]Figure 10
[136]Open in a new tab
(A) Wayne diagram; (B) KEGG pathway analysis; (C) bubble map.
4. Discussion
Walnuts are a type of nut rich in unsaturated fatty acids [[137]43],
which has the efficacy of warming the lungs and fixing asthma, is a
laxative, and promotes glucose utilisation, cholesterol metabolism, and
cardiovascular protection [[138]44,[139]45]. Walnuts are mostly used
for oil extraction, and the by-product walnut meal is rich in proteins.
The extraction of walnut protein from walnut meal is an important
direction for the development of the walnut industry in the future so
as to realise the comprehensive utilisation of walnut meal, improve the
added value of walnut kernel by-products, and reduce the eutrophication
pollution of the environment. It has been found that walnut protein
peptides prepared using different enzymes possess different
physiological activities, including brain-health benefits, antioxidant
properties, anti-cancer effects, anti-ageing effects, promotion of
blood circulation, enhancement of the digestive system, prevention of
radiation, insomnia relief, and improvement of endocrine system
function [[140]46,[141]47,[142]48,[143]49]. This study used WP
enzymatically digested by the oncidium protease, discovered by our
team, to isolate LWPs with a molecular weight of less than 3000 Da.
After gavage of LWP to the mice, the serum levels of BUN, LD, CK, and
LDH of the mice were significantly elevated; in addition, the
high-intensity exercise significantly reduced the food intake, HG, and
MG of the mice but the experimental group that had been intervened by
LWP had a relatively small reduction in HG and MG. This indicates that
our prepared LWP has a certain alleviating effect on exercise fatigue
in mice. The results of HE staining showed that LWP did not have any
toxicological effect on mice and, in addition, the LWP intervention had
a protective effect on skeletal muscle, reducing the damage to skeletal
muscle cells caused by exercise and enhancing the exercise capacity of
mice. In a study of macadamia nut hydrolysates, it was found that
macadamia nut protein hydrolysates significantly prolonged
weight-bearing swimming time, promoted hepatic glycogen synthesis, and
reduced blood urea nitrogen and lactate in mice [[144]50]; however, its
anti-fatigue effect was not as excellent as that of LWP.
Separation and purification is a complex and time-consuming process,
and peptide sequences that may be potentially biologically active can
be screened quickly, efficiently, and cost-effectively by
ultrafiltration separation, which can also reveal the mechanism of LWP
activity at the molecular structure level [[145]51]. A total of 4927
peptide sequences were identified in this study, and these peptides
mainly consisted of 7–20 amino acids, with a predominance of short
peptides with a molecular mass less than 1500 Da (61%). The subcellular
localisation of differentially expressed proteins to the extracellular,
nucleus, and cytoplasm was predominant, which was largely consistent
with the results that cellular components were enriched in the
extracellular region and extracellular vacuoles and were mainly
enriched in the cytoplasm and nucleus. Among the biological functions
mainly performed by LWP, the differential expression lies in the redox
enzyme activity and molecular metabolism, and has a significant impact
on the cellular and intracellular components. In the COG functional
classification, the most frequently occurring proteins were cellular
process and signal transduction proteins, and the least frequently
occurring proteins were information storage and processing proteins.
These results correspond to data from animal experiments.
The maintenance of gut microbial community diversity is very important
for our life activities. In this study, the structural composition of
gut microbes in mice gavaged with LWP was analysed using bacterial 16S
rRNA high-throughput sequencing, and the results showed that there were
no significant differences in the Ace, Chao1, Shannon, and Simpson
indices of gut microbes in all groups. The composition of the mouse
intestinal microbial community was mainly composed of four groups,
Firmicutes, Verrucomicrobiota, Bacteroidota, and Desulfobacterota,
which was the same as in previous studies. Thick-walled bacterial
phylum is the most dominant group of bacteria in the intestine, which
can help in the breakdown of complex carbohydrates, fatty acids, and
polysaccharides in the intestine, and the higher relative abundance of
Thick-walled bacterial phylum in the intestine may be related to the
food source of mice. In this study, the positive control mice were
gavaged with Vc and the experimental mice were gavaged with LWP, and
these two gavages may have influenced the relative abundance of
Thick-walled phyla in the intestinal microbial community of the mice.
In 2004, researchers at the Microbiology Laboratory in Wageningen, The
Netherlands, isolated Akkermansia from the faeces of healthy adults,
and AKK has shown remarkable potential and advantages in the prevention
and treatment of cardiovascular disease, colorectal cancer, and gouty
arthritis. Although AKK bacteria account for only 3–5% of the
intestinal flora, they are strongly associated with disease and health,
and have been described as the “longevity bacterium” or the “strongest
fighter among probiotics” [[146]52]. Muribaculum is a major
mucin-monosaccharide foraging bacterium that prevents the colonisation
of Clostridium difficile while maintaining homeostasis in the
intestinal tract. Alistipes is a Gram-negative bacterium in the phylum
Mycobacterium, a relatively new genus of bacteria with protective
effects against certain diseases, including liver fibrosis, cancer
immunotherapy, and cardiovascular disease [[147]53]. Eubacterium
siraeum is a bacterium associated with adiposity and fatigue but has
been linked to disease and health. Siraeum is a strain associated with
fat deposition, which is effectively inhibited mainly by inhibiting the
PI3K/AKT signalling pathway, which is a key regulator of lipid
metabolism. In this study, we found that Akkermansia, Muribaculum,
Alistipes, and Eubacterium siraeum were significantly increased in the
LWP group, suggesting that consumption of LWP may be beneficial for
long-term health.
In this study, we analysed the effect of LWP on faecal metabolic
profiles using LC/MS. We identified 108 differential metabolites and
four significantly enriched pathways. Palmitoylethanolamide, a natural
human endogenous nutrient, has shown promising potential in relieving
various pains, with anti-inflammation and neuroprotection properties
[[148]54], while 4-Methyl-5-thiazoleethanol is a common aroma
ingredient in gastronomy, an ingredient with a brothy, nutty, and
strong odour naturally found in beer, cocoa, and citrus fruits. In
metabolite analyses of LWP-H mice, we found significant up-regulation
of both Palmitoylethanolamide and 4-Methyl-5-thiazoleethanol. As
outlined by the KEGG pathway enrichment analysis, LWP was significantly
enriched in glycerophospholipid metabolism, biosynthesis of amino
acids, and choline metabolism in cancer, and the metabolism of the
related diseases may all be related to the degradation of LWP. LWP
regulates a variety of physiological processes by affecting the
metabolism of small molecule compounds but its regulatory mechanism
needs further investigation. To determine its generalizability, the
anti-fatigue activity can also be subsequently validated using other
animal models or clinical trials. Therefore, LWP is not only easy to
prepare and economical but also has good anti-fatigue effects, and its
use in natural dietary supplements can add a unique flavour. In the
future, it can be used to produce health food for fitness personnel,
athletes, or people prone to fatigue.
5. Conclusions
In this study, we used Dregea sinensis Hemsl. Protease to enzymatically
digest WP to obtain WPHs, identified as LWP after ultrafiltration, and
then investigated its anti-fatigue effect and its effect on the
intestinal microbiology and metabolic profiles of mice by gavaging mice
with different doses of LWP. The experimental results showed that WPHs
are rich in glutamic acid, which is an amino acid that can improve
exercise fatigue. In the characterisation of LWP, differential protein
expression was found to be concentrated in antioxidant-related
pathways. The gavage of mice using LWP for 28 days revealed that LWP
alleviated the rate of weight gain and prolonged the duration of
weight-bearing swimming. The mice showed a significant increase in
glycogen storage in muscle and liver, a decrease in serum levels of
BUN, LDH, LA, and CK, an increase in levels of SOD and GSH, and a
decrease in levels of MDA, predicting that the anti-fatigue efficacy of
LWP is related to its ability to elevate oxidative stress in mice. In
addition, LWP could increase the number of dominant strains, such as
Akkermansia—a strain that enhances immunity and, thus, boosts
anti-fatigue efficacy; furthermore, LWP restructured the gut microbiota
by increasing the abundance of Alistipes, Eubacterium, and Muribaculum.
Based on LC-MS metabolomics results, LWP was mainly enriched in
glycerophospholipid metabolism and biosynthesis of amino acids, with
notable metabolites including Palmitoylethanolamide and
4-Methyl-5-thiazoleethanol, all of which are beneficial to health.
Therefore, LWP can delay the onset of fatigue to a greater extent and
may be used as a natural dietary supplement due to its good efficacy
and ease of extraction.
Author Contributions
S.H.: Formal analysis, Investigation, Software, Writing—original draft.
Y.W.: Formal analysis, Methodology, Software. M.L.: Software,
Validation, Conceptualization. H.M.: Software, Visualization. C.T.:
Data curation. M.W.: Formal analysis. F.Z.: Writing—review and editing.
J.S.: Project administration, Funding acquisition. Y.T.: Resources,
Supervision. C.Z.: Methodology, Supervision. All authors have read and
agreed to the published version of the manuscript.
Institutional Review Board Statement
The animal experiments in this study were approved by the Animal Care
Committee of the College of Animal Science and Technology, Yunnan
Agricultural University (Ethical Review No. 202209012, date: 12
September 2022).
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in the study are included in the
article material, further inquiries can be directed to the
corresponding author.
Conflicts of Interest
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.
Funding Statement
This work was supported by grants from the Yunnan Province-city
Integration Project (202302AN360002), Yunnan Innovation Team of the
Food and Drug Homologous Functional Food Selection of High-level
Scientific and Technological Talents and Innovative Teams Project
(202305AS350025), and the Yunnan Science and Technology Mission of the
Walnut Industry in Fengqing (202204BI090012).
Footnotes
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References