Abstract
In this study, the impact of lentil hull soluble dietary fibers (SDFs)
on colitis and behavioral deficits in mice was assessed. Structural
characterizations of SDFs confirmed that cellulase-modified soluble
dietary fiber exhibited better physicochemical properties: more porous
microstructure; similar polysaccharide structure; more stable particle
size distribution; higher crystallinity; better adsorption capacity;
and lower viscosity. Additionally, we explored its potential cognitive
benefits via the gut-brain axis by behavioral tests, histopathology,
16S rRNA sequencing, gas chromatography and metabolomics analysis. The
results showed that SDFs significantly improved inflammatory symptoms
in colon and brain and cognitive behaviors. LSDF had better efficacy
than HSDF. LSDF intervention decreased the harmful bacteria abundance
(Bacteroides, Flexispira and Escherichia, etc.) and increased
beneficial bacteria abundance (Aggregatibacter and Helicobacter, etc.).
LSDF also affected brain metabolites through the sphingolipid
metabolism. Spearman correlation analysis showed that there was a
positive correlation between harmful bacteria with inflammatory factors
(LPS, IL-1β, IL-6, and TNF-α, etc.) and sphingolipid metabolites, while
beneficial bacteria were positively correlated with brain-derived
neurotrophic factor (BDNF), IL-10, and cognitive behavior. This study
highlights the value of SDFs in future diet-based therapeutic
strategies targeting gut-brain interactions.
Keywords: lentil hulls, soluble dietary fiber, colitis, cognitive
impairment, sphingolipid metabolism, gut-brain axis
1. Introduction
Pulses have long been of great interest in developing functional foods
for health all over the world, and lentils (Lens culinaris L.) are a
globally crucial traditional dietary legume, with about a quarter of
their yield coming from Canada [[30]1]. The dehulling process of
lentils generates 20–28% of the total lentil processed hulls as a
low-value by-product, resulting in the wastage of a promising
health-promoting food ingredient—dietary fiber [[31]2]. Lentil hulls
are composed of 60–90% dietary fiber, 3% ash, 2–8% protein, and 1–3%
lipids, making them a rich source of prebiotics [[32]2]. The basic
components of dietary fiber include pectin, cellulose, mannans, and
xylans [[33]3].
Dietary fibers from different sources have distinct chemical structures
and fermentation properties, with significant variability in their
ability to modulate inflammation in inflammation bowel disease (IBD)
[[34]4]. IBD is a group of chronic inflammatory bowel diseases (IBDs)
that primarily include ulcerative colitis (UC) and Crohn’s disease
(CD). This study focused on the effect of lentil bean hull SDFs on mice
UC and its cognitive impairment through gut-brain axis. Based on its
solubility in hot water, dietary fiber can be further classified into
two types: insoluble dietary fiber (IDF) and soluble dietary fiber
(SDF), each with unique biological functions. SDF consists of a number
of oligosaccharides and indigestible polysaccharides such as pectin,
inulin, Arabic gum, and β-glucan, while lignin, hemicellulose, and
cellulose constitute IDF [[35]5]. Compared to IDF, SDF provides greater
viscosity during food processing and is commonly used as an emulsifier
or gelling agent [[36]6]. As a unique polysaccharide, SDF has been
shown to enhance the abundance of Bifidobacterium in the gut,
effectively alleviating colitis [[37]7]. SDF acts as a fermentation
substrate for microorganisms, producing short-chain fatty acids
(SCFAs), which have been proven to reduce inflammation and reinforce
the intestinal mucosal barrier, thereby alleviating UC inflammation
[[38]8,[39]9]. Meanwhile, SDF intake has also been found to improve
cognitive performance. For instance, 5% fructans were found to be
effective in altering the gut microbiota structure and increase SCFA
levels, enhancing cognitive function in the male Alzheimer mice
[[40]10]. However, research on how SDF from natural plant sources, such
as lentil hulls, affects brain function and behavioral aspects in the
alleviation of colitis, and how brain metabolite reciprocally affect
colitis, remains limited.
Ulcerative colitis (UC) has become a global health burden, with an
estimated 5 million cases worldwide in 2023 [[41]11]. According to
epidemiological research, the incidence rates are stabilizing or
decreasing in the majority of nations with high incomes in North
America, Northern Europe, and Australia, while sharply increasing in
countries with low or middle incomes in Asia [[42]11]. Treatment
options for UC include aminosalicylates for mild to moderate cases,
immunosuppressants for moderate to severe cases, and steroids for acute
episodes [[43]12]. The current therapeutic goals for UC focus on
halting disease progression and preventing inflammation-induced flares
[[44]13]. However, these medications often have side effects and
limited efficacy, driving interest in plant-sourced dietary treatment
with fewer adverse effects. Many studies have identified that SDF could
assist those diagnosed with colitis. SDF from Quinoa bran was proven to
alleviate the symptoms of colitis and improve the diversity of gut
microbial community [[45]14]. SDF has been reported to exhibit higher
antioxidant capability, thermal stability, and water-holding capacity
than cellulose. These characteristics might be explained by the altered
monosaccharide content, thinner particle size, and more porous
structure of SDF. SDF was subsequently found to reduce the clinical
signs of colitis mice based on these functional characteristics.
[[46]15].
Gut microbiota not only affects the host’s immune system [[47]13], but
also contributes to the synthesis of metabolites and neuroactive
factors [[48]16]. Studies have shown that certain members of the
Bacteroidetes are able to break down polysaccharides with higher
molecular weights, and then those small molecular weight sugars are
utilized by the Firmicutes to provide energy for other microorganisms
[[49]17]. Akkermansia muciniphila has been shown to hydrolyze
carbohydrates from the colon’s mucus layer, using them as substrates to
generate acetate and oligosaccharides [[50]18,[51]19]. SCFAs also have
important effects on the nervous system. For instance, studies have
demonstrated that sodium butyrate promotes the expression of BDNF,
neural proliferation, and neurogenesis of mice [[52]20,[53]21].
Propionate is important for normal brain development and behavioral
control, which is implicated in neurodevelopmental disorders such as
autism [[54]22,[55]23].
The primary mediators of gut–brain axis communication are metabolites
(e.g., SCFAs), the vagus nerves, hormones (e.g.,
hypothalamic–pituitary–adrenaline), and immune activation which are
neurological pathways generated by the gut microbiota [[56]24]. The
disruption of the gut microbiota structure alters the concentrations of
inflammatory agents and metabolites, including lipopolysaccharides
(LPSs) and pro-inflammatory cytokines, which then cause altered levels
of neuroinflammation by increasing the permeation of the gut and the
barrier between brain and blood [[57]25]. Emerging evidence
demonstrates that peripheral inflammatory factors in IBD patients also
promote the development of various disorders of the central nervous
system, including depression and anxiety [[58]26]. The opening of the
gut vascular barrier, which connects the gut and the liver, can spread
gut inflammation to the brain, which can result in mental impairments
like anxiety and depression [[59]27]. An essential scientific technique
for researching the gut–brain axis mechanism is metabolomics. A study
assessed the relationship of brain neurotransmitters and gut
metabolites in gnotobiotic mice in vivo based on an LC-MS/MS-based
targeted metabolomics approach [[60]28]. Recently, there has been a
growing interest in investigating the gut–brain axis in relation to
cognitive and colitis issues. For instance, through the
microbial-gut-brain axis, the carotenoid component lycopene (LYC) has
been found to have a beneficial preventive effect on colitis and
associated disorders of behavior [[61]29]. Apple polyphenols have been
demonstrated to repair colitis and related cognitive deficits via
strengthening the gut barrier, reducing inflammation, repairing
neurological impairment and gut microbiota composition, and modulating
circadian rhythms [[62]30]. Furthermore, probiotic supplementation
suppressed neuroinflammation in DSS-induced colitis mice by reducing
inflammation, as well as modulating neurotransmitters [[63]31].
Current research to develop interventions and therapeutics targeting
related sphingolipids is generating interest. Glycerophoslipids,
polyunsaturated fatty acids, and sphingolipids are among the substances
that are crucial in causing depressive symptoms [[64]32]. Moreover,
sphingolipids, lysophospholipids, and phosphatidylcholine were among
the metabolites that were found to be altered in the brains of sad mice
in earlier research [[65]33]. Sphingomyelin (SM), ceramide (CER),
sphingosine (SPH), and sphingosine-1P (S1P) are important metabolites
of some sphingolipid metabolic pathways. Recent metabolomics suggests
that sphingomyelin (SM) contributes as a biomarker for IBD diagnosis
[[66]34]. Ceramides (CERs) are a component of the intestinal epithelial
cell membrane and may serve as a prospective biomarker for UC [[67]35].
Levels of specific ceramide subgroups (e.g., C18:0-Cer, C20:0-Cer, and
LacC16-Cer) are closely related with UC, this might be helpful in
differentiating UC patients from healthy individuals [[68]36].
Sphingosine is involved in signaling processes related to cell survival
and neurodegeneration, with sphingomyelin and ceramide being the
precursors of sphingosine, which is phosphorylated by SPHK gene to
produce S1P [[69]37]. According to recent research, sphingosine-1P
(S1P) has a role in the pathophysiology of disorders affecting the
brain, particularly those involving the transmission of synaptic
information, neuroinflammation, and neuronal autophagy [[70]38].
Overall, one potential therapeutic approach for the treatment of
depression has been the modulation of the sphingolipid metabolism.
Although some studies have explored how dietary fiber affects
intestinal health and cognitive decline, studies investigating the
mechanism of action of SDFs extracted from plant sources on colitis
through the gut–brain axis are still very limited [[71]4,[72]8,[73]10];
hence, this study aims to fill this gap. This is the first study to
characterize the structure of SDFs from lentil hulls, in particular,
comparing the structural differences between SDFs obtained by two
different enzymatic methods, which reveals the structural basis of
their mechanism in alleviating colitis and cognitive impairment. We
then utilized the sodium dextran sulfate (DSS)-induced cognitive
impairment in a mouse model of colitis to assess the functional
activity of SDFs. Using microbiomics and a metabolomic analysis of
brain tissues, we explored the potential mechanisms by which SDFs in
lentil hulls regulate metabolic pathways and inflammatory responses
through the gut–brain axis. This study is the first to investigate the
nutritional effects of SDFs derived from lentil hulls on DSS-induced
colitis and behavioral deficits in mice, and provides a better
theoretical basis for future population-based experiments.
2. Materials and Methods
2.1. Materials and Reagents
The Canadian International Grains Research Institute (Winnipeg, MB,
Canada) supplied green lentil hulls. Lentil hulls were dried at 60 °C,
crushed with a small high-speed pulverizer (Hebei Benchen Science and
Technology Co., Ltd., Hebei, China), the raw material for this study
were then acquired after passing through 120-mesh filter. The materials
were kept at −4 °C after being finely powdered. MP Biomedicals (Irvine,
CA, USA) provided the dextran sodium sulfate (36–50 kDa). Aladdin
(Shanghai, China) provided reference stand of SCFAs. We purchased BDNF
and LPS ELISA kits from Jiangsu Meimian Industrial Co., Ltd. (Yancheng,
China).
SDFs include two types of soluble dietary fiber: SDF and SDFM. In this
study, SDF is a soluble dietary fiber obtained using classical
enzymatic methods [[74]39], specifically using three enzymes, including
thermostable α-amylase, protease, and saccharolytic enzyme. On the
other hand, SDFM is modified soluble dietary fiber obtained with the
addition of cellulase, specifically using four enzymes, including
thermostable α-amylase, protease, saccharolytic enzyme, and cellulase.
The full description of the SDF and SDFM preparation process:
initially, 10 g dried lentil hulls were ground and sieved (200 μm), the
milled hulls were sequentially mixed with petroleum ether (1:25 w/v)
and 85% ethanol (1:10 w/v) with homogenizer to remove the fat and free
sugar, respectively. After drying in a vacuum oven, the hulls were
stirred and suspended in phosphate buffer salts (PH = 6, 1:20 w/v) and
then the mixture was ultrasonically modified in an ultrasonic machine
(35 min, 15 °C, 100 W). Then, the samples were hydrolyzed with the
addition of 2.5 mL thermostable α-amylase (PH = 6, 5 min, 100 °C).
After cooling to 50 °C, 30 mg protease (PH = 6) was added and the
mixture was hydrolyzed for 35 min. The PH was adjusted to about 4.5
with the addition of 5 mL 3 mol/L acetic acid and then 30 μL of
saccharolytic enzyme was added, followed by hydrolyzing for 30 min at
60 °C. The solution was boiled for 10 min to deactivate the enzymes and
then centrifuged at 4000× g for 15 min. The supernatant was decanted
and filtered through a sintered glass funnel containing diatomaceous
earth, then evaporated to 1/4 of the original volume using a rotary
evaporator at 65 °C. The concentrated supernatant was mixed with 4
times the supernatant volume of 95% ethanol (preheated to 60 °C), and
SDF was precipitated by refrigeration at 4 °C overnight and centrifuged
at 4500× g for 10 min. Thereafter, the residue was redissolved in water
and subjected to the spinning step once more and freeze-dried. The
dried samples were washed with 100 mL of 95% ethanol and acetone, and
subsequently dried in a vacuum oven at 80 °C, there after ground to
powder and stored in a desiccator. SDFM was synthesized by adding 30 mg
of cellulase and incubating at 50 °C for 1.5 h following the completion
of hydrolysis by the saccharase enzyme. Both the preceding and
following steps were consistent with those previously described.
2.2. Structural Characterizations and Functional Properties of SDFs
2.2.1. Scanning Electron Microscopy (SEM)
SDFs were examined at 5 kV for surface and microstructure using an SEM
(Quanta200F, FEI, Hillsboro, OR, USA). Prior to observation, the
dehydrated samples were covered with a 100 μm thick coating of gold
after being sprinkled on a support using double-sided conducting
adhesive tapes. SDF micrographs were captured at a magnification of
3000×.
2.2.2. Fourier Transfer-Infrared Spectrometry (FT-IR)
FTIR analysis of SDFs was obtained by a Thermo Nicolet 5700 instrument
(Thermo Fisher Scientific, Waltham, MA, USA). The SDF powder was
blended with potassium bromide powder (1:100, w/w), and the scan range
was set from 400 to 4000 cm^−1 during 32 scans.
2.2.3. Particle Size
The distribution of particle sizes of SDFs was measured using a laser
particle size analyzer (Malvern Mastersize 2000, Malvern Panalytical
Ltd., Malvern, UK). SDFs was formed into a solution of 1 mg/mL
concentration with ultrapure water, and then, the results were tested
at room temperature after 20 min of sonication.
2.2.4. X-Ray Diffraction (XRD)
To identify the crystalline state of SDFs, an X-ray diffractometer
(Empyrean, Malvern Panalytical, Almelo, The Netherlands) was employed.
With a resolution of 0.02° and a scan angle of 2θ = 5–60°, it was run
at 10 mA and 30 kV. To determine the relative crystallinity of SDFs,
the Segal method was applied [[75]40].
2.2.5. Adsorption Capacity of Glucose and Cholesterol
Some studies reported that SDF may be able to modulate intestinal
microbial metabolism by adsorbing glucose to the concentrations of
metabolites including SCFAs, which in turn improves the intestinal
inflammatory response [[76]41,[77]42]. Moreover, it has been found that
cognitive impairment is closely related to hyperglycemia [[78]43], and
by regulating blood glucose levels, SDFs may also play an indirect role
in the protection of cognitive function. Referring to the method of
[[79]44], 0.5 g SDF was melted in 50 mL of glucose solution (100 mM/L)
and then incubated at a thermostatic shaking incubator (37 °C, 6 h),
centrifuged (4500 rpm/min, 15 min), the concentration of glucose of
supernatant was detected at 505 nm using the DNS colorimetric method.
The glucose concentration in the initial glucose solution was recorded
as a and the supernatant’s glucose concentration (b) was noted. The
following formula was used to calculate the glucose adsorption capacity
(GAC) (unit: mg/g):
[MATH: GAC=a−b<
mrow>m :MATH]
(1)
The study reported that intestinal concentrations of cholesterol are
linked to intestinal inflammation [[80]45], and cognitive impairment is
also thought to be closely linked to abnormalities in the metabolism of
lipids [[81]46]. SDFs were added into two egg yolk solution (fresh egg
yolk mixed with 9× volume distilled water), whose pH was adjusted to 2
and 7, respectively, shaken (80 rpm/min, 37 °C, 2 h) with a shaker,
centrifuged (15 min, 4500 rpm/min), and left, after which the
phthalaldehyde approach was used to determine the supernatant’s
cholesterol level. Using the following formula, the cholesterol
adsorption capacity (CAC) (unit: mg/g) was determined, where C[1] and
C[0] are the cholesterol content of the egg yolk solution and its
supernatant after centrifugation without SDFs, respectively; C is the
cholesterol content of the supernatant after the addition of SDFs.
[MATH: CAC=[C0−C−C1−C0
]×25m :MATH]
(2)
2.2.6. Flow Behavior
Static Rheology of SDFs
At 55 °C, the SDFs were dissolved in distilled water to a 40 mg/mL
concentration while being continuously stirred for 30 min. To
equilibrate and release any trapped air, the solution was then kept at
4 °C for 12 h. A DHR-2 rheometer (TA Instruments, New Castle, DE, USA)
is employed to determine the solution’s viscosity at shear rates
between 0.1 and 100/s followed by equilibration at 25 °C for 5 min.
Dynamic Rheology of SDFs
Test samples were prepared by dissolving 100 mg SDFs in 10 mL distilled
water with constant stirring. The SDF gel was then poured into a
rheometer plate and allowed to equilibrate for 2 min. Silicone oil
droplets were placed at the border of cone to prevent evaporation
throughout tests. To measure changes of stored energy modulus
elasticity (G′) and loss modulus viscosity (G″) with angular frequency
from 0.1 to 10 Hz, strain scanning tests were conducted using a DHR-2
rheometer (TA Instruments, New Castle, DE, USA) at 25 °C and 1% strain
at a range of 0.1–10 Hz.
2.3. Animals, DSS-Induced Colitis Model, and SDF Intervention
We acquired 48 male C57BL/6J mice from SPF Bio-Technology Co., Ltd.
that were 6–8 weeks old. Mice lived under conditions of a 12 h
light/dark cycle, a temperature of 20 ± 2 °C, and a humidity of 55 ±
5%. Each cage contained 4 mice. The Ethics Committee approved the use
of animals in the Jiangxi University of Chinese Medicine Experimental
Animal Science and Technology Centre (JZLLSC20220492). After 7 days of
acclimation and housing under controlled conditions, to examine the
effect of SDFs on UC, each of the four groups (n = 12 mice) was
randomly assigned to either CON, DSS, LSDF treatment (Gavage 500
mg/kg/d of b.w. SDF), or HSDF treatment (Gavage 1000 mg/kg/d of b.w.
SDF). All mice were given the standard diet (AIN-93G), the CON group
mice were given distilled water for the duration of the trial, whereas
the mice in the remaining groups received distilled water for seven
days prior to receiving 2% DSS for seven days. On the 15th day, the
open field test (OFT) and new object recognition test (NORT) were
administered. The elevated plus maze test (EPMT) and Y-maze test (YMT)
was conducted on the 16th day. [82]Figure 1 displays the animal
experimental design.
Figure 1.
[83]Figure 1
[84]Open in a new tab
The protocol scheme diagram of colitis mouse model.
2.3.1. Behavioral Tests
OFT
The OFT was conducted to estimate the locomotor activity and anxious
behavior in mice. After being first positioned in a corner, each mouse
was afforded 10 min for unrestrained exploration. The box was virtually
divided into sixteen quadrans, with the four central sections
specifically marked as the central sections. The specific device
schematic is shown in [85]Figure 2A. KEMaze software version number 1.0
(Nanjing Karvin Biotechnology Co., Ltd., Nanjing, Jiangsu, China) was
used to record and examine the overall distance travelled, the number
of admissions into the center, and the duration of time spent there.
Figure 2.
[86]Figure 2
[87]Open in a new tab
Diagrammatic representation of experimental setup: open field test (A),
new object recognition test (B), elevated plus maze test (C), and
Y-maze test (D).
NORT
NORT is based on animals’ innate interest in novelty and uses their
exploratory behavior towards new objects to measure their ability to
perceive and remember changes in their environment. At the beginning,
the mice were put in a square box containing two similar cylinders a
and b. Two hours later, the mice were reintroduced to the same
environment; however, a new square item, c, took the place of one of
the original objects, a. The mice were free to explore the environment
the entire time and interact with the two similar objects. Usually,
more time spent exploring the new object is seen as an indicator of
better memory function. The specific device schematic is shown in
[88]Figure 2B.
EPMT
Two opposed open arms (a and b, each measuring 6 cm × 30 cm) and two
opposed closed arms (c and d, each measuring 6 cm × 10 cm × 30 cm)
constituted the EPMT device. The specific device schematic is shown in
[89]Figure 2C. The device was positioned 70 cm above the floor. Every
mouse was put in the middle of the device (6 cm × 6 cm), facing with
the open arm, and each test lasted for 5 min. The number of entries,
total dwell time, and distance in open arms (a and b) were recorded
within 5 min.
YMT
The mice’s spatial cognitive performance was assessed using the Y-maze
test. Three identical arms, each measuring 28 cm in length, 10 cm in
height, and 5 cm in width, were joined at a 120° angle. A specific
device schematic is shown in [90]Figure 2D. During the learning period,
the a arms was closed, each mouse was set at the terminus of the b arm
and given three minutes to wander around. Arm a was open two hours
later, and then the earlier process was carried out once more. Reduced
anxiety-like behaviors were indicated by more entrances and more time
spent in open arms.
2.3.2. Disease Activity Index (DAI) Assessment
In this investigation, the DAI score was calculated by averaging the
three indicators: rectal bleeding, stool consistency, and weight loss
[[91]39], and the details are shown in the [92]Table S1.
2.3.3. Experimental Records and Sample Collection
During the experimental, the mice’s body weight was recorded every day.
Blood was obtained by orbital blood sampling and centrifuged to obtain
the supernatant serum. Then, the mice underwent cervical dislocation
after being anesthetized with isoflurane on the sixteenth day. After
that, the brain, feces, colon, and colon contents were all
systematically gathered. Colonic length was obtained by measuring with
a stainless steel straightedge.
2.3.4. Serum LPS and BDNF Level Detection
The ELISA kits (Wuhan Elabscience Co., Ltd., Wuhan, China) were
utilized to measure the concentrations of BDNF and lipopolysaccharide
(LPS) in serum.
2.3.5. Hematoxylin and Eosin (H&E) Staining of Colon and Brain
To evaluate the histological damage of colitis and brain, the whole
brain and a small fragment of the colon (0.5 cm) were collected. Using
the appropriate procedures, sections embedded in 5 μm paraffin were
made and analyzed with H&E staining. Images of the staining results
were obtained by a camera (Nikon Co., Tokyo, Japan).
2.3.6. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)
By applying the RNAeasy^TM Animal RNA Isolation Kit (Beyotime Biotech
Inc., Shanghai, China), total RNA was isolated from the mice’s brain
and colon tissue according to the manufacturer’s guidelines. Following
the use of a Nano Drop 8000 quantifier (Thermo Fisher Scientific) to
verify the RNA’s quantity and purity, all the RNA was diluted to the
same concentration. The PrimeScript RT Reverse Transcription Kit
(Takara Biotechnology Co., Shiga, Japan) was used to reverse-transcribe
RNA to generate cDNA, using a PCR Thermal Cycler (T100RT, Bio-Rad
Laboratories, Inc., Hercules, CA, USA), with the following cycle
parameters: 25 °C for 5 min, 50 °C for 15 min, 85 °C for 5 min, and
ultimately 4 °C for 10 min. Using a Fluorescent Quantitative PCR
Instrument (CFX Connect^TM), RT-qPCR was carried out, and the primer
sequence list for BDNF, TNF-α, Tlr4, IL-6, NF-κB, IL-10, IL-1β, SGMS1,
SGMS2, CERS4, CERS6, SPHK1, and SPHK2 was provided on [93]Table S2.
2.3.7. Gut Microbiota Analysis
Gene sequencing was commissioned to Personal Biotechnology Co., Ltd.
(Shanghai, China). First, DNA from the gut microbiota was obtained and
tested for concentration and purity, according to the manufacturer’s
instructions. The V3–V4 region of the 16S rRNA gene was subsequently
amplified using PCR. The amplicon paired end 2 × 250 bp sequencing was
purified, quantified, and then sequenced. The output results were then
examined on the QIIME2 platform.
2.3.8. Measurements of Fecal SCFAs
According to the previous method [[94]39], 100 mg of the fecal sample
or the stand solution was combined with 700 μL deionized water,
followed by adding 35 μL of 10% sulfuric acid solution and sonicating
for 10 min in an ice bath (100 W). After being centrifuged for 15 min
(10,000× g and 4 °C), the supernatant was obtained for analysis. A 0.22
μm filter membrane was used to filter the combined supernatant after
the precipitate was mixed with 0.7 mL of water and the previous step
was repeated. The content of SCFAs were detected using a gas
chromatograph and the DB-WAX capillary chromatographic column (30 m ×
0.25 mm × 0.25 μm, Lanzhou, China). The supernatant was injected into
the inlet at 240 °C with an 8:1 split ratio. The temperature
methodology was identical to the manufacturer’s instructions, as
described in a previously published reference [[95]47]. Using the
standard curve, sample peak areas were determined to represent the SCFA
content in feces.
2.3.9. Untargeted Metabolomics Analysis
In short, twenty milligrams of cerebral cortical brain tissues were
homogenized with 400 μL ice-cold methanol on a TissueLyzer (Qiagen,
Hilden, Germany) at 60 Hz for 3 min and then followed by an hour of
incubation at −20 °C. After centrifugation for 10 min (15,000× g and 4
°C), the supernatant was carefully aggregated, blow-dried, and then
resuspended in 50 μL ACN–water = 2:98. The primary metabolites profiled
in the brain were identified by the Q Exactive Focus system equipped
with a Thermo-Accucore C18 column (2.1 × 100 mm; 2.6 μm, Waters,
Waltham, MA, USA). Acetonitrile (B) and formic acid (A) at 0.1% made up
the mobile phase. [96]Table S3 displays the specific gradient elution
settings. The mobile phase flow rate was 0.4 mL/min, the injection
volume was 2 μL, and the column temperature was 40 °C. The following
parameters were set for the ESI source: capillary temperature at 325
°C, spray voltage at 2.75 kV, aux gas heater temperature at 400 °C, and
scanning mass range at 80–1000 m/z, a full MS resolution at 70,000, and
sheath gas flow rate at 60 mL/min, aux gas flow rate at 10 mL/min, and
sweep gas flow rate at 1 mL/min.
Progenesis QI (version 2.4) was used to transform the raw MS data, and
measurements were made of peak detection, matching, and comparison.
Initially, the Human Metabolome Database (HMDB) was applied in order to
annotate the metabolites. Orthogonal partial least-squares discriminant
analysis (OPLS-DA) and pareto-scaled principal component analysis (PCA)
were conducted with Simca (version 14.1). The variables of importance
(VIP) were calculated to assess each variable’s contribution to
categorization in the OPLS-DA model. The significance of the
differences between groups was evaluated using the Student’s t-test. To
determine whether metabolites may be classified as differential
metabolites, the following criteria were used: VIP value > 1, p-value <
0.05, and |Fold Change| > 2. Biomarker screening and functional
annotation analysis (KEGG pathway annotation analysis, metabolic
pathway enrichment analysis) were subsequently employed to investigate
the significantly different metabolites. Spearman correlation analysis
was utilized to evaluate the relationship between intestinal microbiota
and markers linked to colitis and cognition.
2.4. Statistical Analysis
All results were expressed as the mean ± standard error of the mean (X
± SEM). SPSS 26.0 (SPSS Inc., Chicago, IL, USA) was used for the
statistical analysis of the experimental data. One-way analysis of
variance was used for statistical significance evaluated, followed by
Duncan test to evaluate the variations in data between groups. For 16S
rRNA sequencing and metabolomics data, a Wilcoxon rank sum test and
t-test were used for significant differences among different groups,
respectively. When the p-value < 0.05, the data were identified as
statistically significant difference.
3. Results
3.1. Scanning Electron Microscopy Observation
In this study, soluble dietary fiber was extracted by two methods,
mainly to investigate the effect of cellulase addition on the
structural properties of soluble dietary fiber from lentil hull. SDFM
(modified soluble dietary fiber) was extracted with cellulase addition,
while SDF (soluble dietary fiber) was extracted with classic enzymatic
methods without cellulase. [97]Figure 3A,B show the different
properties of SDF and SDFM under 3000× magnification. It can be
observed that the particles of SDF and SDFM were round, oval in shape
and small. SDFM showed the enzymatic hydrolysis of the cellulose effect
with more cracks appearing on surface and in a larger size compared to
SDF. The changes in the microstructure of SDFM compared to SDF were
probably a result of the enzymatic process of cellulase. The enzymatic
digestion may cleave the SDFM surface into more crevices and lead to
the aggregation of SDFM extracted from lentil hulls. Surface morphology
results may be associated with diverse structures. A more sparse and
porous structure means that the SDFM has increased the specific surface
area, which enables it to more effectively adsorb substances such as
cholesterol and glucose [[98]48].
Figure 3.
[99]Figure 3
[100]Open in a new tab
The structural characterizations and functional properties of SDFs. (A)
The scanning electron microscopy of SDF, scale bar: 10 μm; (B) The
scanning electron microscopy of SDFM, scale bar: 10 μm; (C) The Fourier
transfer infrared spectrometry of SDF and SDFM; (D) The particle size
of SDF and SDFM; and (E) The X-ray diffraction spectrometry of SDF and
SDFM.
3.2. Fourier Transform Infrared Spectroscopy (FT-IR) of SDFs
[101]Figure 3C’s infrared absorption spectra demonstrate that the
wavelength distributions of soluble dietary fiber (SDF) and modified
soluble dietary fiber (SDFM) are similar. The wide absorption signals
of SDF and SDFM at around 890 cm^−1 indicate that glucose has a β
configuration [[102]49]. Each of these peaks was a typical cellulose
structure peak. For both samples, the main peaks at about 3400 cm^−1
and 2500 cm^−1 are attributed to the C–H absorption of the rings and
the O–H stretching absorption carried on by the intramolecular and
intermolecular hydrogen bonding of uronic acids, respectively
[[103]50]. And, the absorption peak near 3400 cm^−1 usually corresponds
to the stretching vibration of C–H. The presence of C–H helps soluble
dietary fiber to form a gel-like substance in the intestinal tract
[[104]51], delaying the absorption of sugars and lipids, and thus
showing the biological activities of lowering blood glucose and blood
lipids. The spectrum area in the 900–1200 cm^−1 range, which represents
the skeletal C–O and C–C vibration bands of glycosidic bonds and the
pyranose rings, reflects similarities in the neutral sugar content of
the separated polysaccharides [[105]52]. At the same time, the
absorption peaks between 900 and 1200 cm^−1 correspond to the
stretching vibration of C–O, indicating that soluble dietary fiber
contains hemicellulose, which contributes to the structural stability
and adsorption capacity of dietary fiber [[106]53]. Furthermore, there
are bands of absorption at 2972 cm^−1 (asymmetric bending) and 2862
cm^−1 (symmetric bending) which correspond to the C–H of the methyl
ethers of galacturonic acid. The prominent absorption band of soluble
dietary fiber at 1050 cm^−1 were attributed to C–C, C–O, and the
glycosidic C–O–C stretching, which are commonly reported for xylans and
arabinoxylans [[107]54,[108]55]. The presence of these functional
groups indicates that SDFs have a characteristic absorption peak with a
polysaccharide structure, which contributes to the water-holding and
swelling properties of the dietary fiber [[109]56], thus enhancing its
bioactivity in regulating intestinal function and lowering cholesterol.
3.3. Particle Size Analysis Results of SDFs
The results of the particle size of soluble dietary fiber from lentil
hulls are shown in [110]Figure 3D. The average particle size of the two
samples was around 500 nm, and the particle size distribution map of
SDFM showed one large single peak and two small peaks, while SDF showed
only one large single peak, which exhibited a more uniform particle
size distribution system. The appearance of two small peaks indicates
that there is a new oligosaccharide or monosaccharide production in the
cellulose enzymatic digestion process, which changes the molecular
structure of SDFM and results in a higher surface area and more active
sites [[111]57]. In addition, the newly produced oligosaccharides or
monosaccharides can act as prebiotics to promote the growth and
reproduction of beneficial intestinal bacteria. And, they can produce
short-chain fatty acids after being fermented in the intestines
[[112]58], thus regulating the balance of intestinal microbiota and
enhancing intestinal health.
3.4. X-Ray Diffraction (XRD) Analysis Results of SDFs
[113]Figure 3E showed the X-ray diffraction (XRD) intensity profiles
and crystallinity variation of soluble dietary fiber (SDF) and modified
soluble dietary fiber (SDFM) samples. According to XRD analysis, SDF
exhibited a strong diffraction peak at approximately 45° and weak
diffraction peak at approximately 24°, while SDFM only had a strong
diffraction peak at 24°. A faint diffraction peak that emerged at
around 42.66° belonged to a characteristic diffraction peak of
cellulose type I [[114]59]. The typical cellulose I type diffraction
angles, with a strong peak in the (002) diffraction direction at 24°,
might have overlapped with the hemicellulose peak [[115]59,[116]60].
The diffraction peak intensities of the SDFM samples were generally
higher than those of the SDF samples, suggesting that the addition of
cellulase increased the diffraction intensities of some specific
crystal planes. And the cellulase-treated sample’s peak was sharper at
24° than the untreated sample, indicating that the enzymatic hydrolysis
of cellulase improved crystallinity, similar to the previous reports
[[117]61]. The core of crystalline cellulose is made up of a linear
chain of β (1–4) connected D-glucopyranose units. There are two
portions of it: crystalline and amorphous [[118]62]. Hemicellulose is
typically attached to cellulose microfibrils, but has a random and
amorphous structure with low strength and is easily hydrolyzed by
several hemicellulases [[119]63]. The increase in crystallinity is
primarily attributed to the hydrolysis of hemicellulose and the
amorphous portion of cellulose [[120]61,[121]64]. The degree of
crystallinity of SDFs is an important structural characteristic that
influences their biological activity [[122]65]. Higher crystallinity
results in higher stability and a longer residence time in the gut
[[123]66], thus enhancing its ability to adsorb substances such as
cholesterol and glucose. In addition, it has been shown that changes in
crystallinity affect the antioxidant capacity of soluble dietary fiber.
For example, the soluble dietary fiber of coffee peer-modified by
ultrasound had increased crystallinity and its free radical scavenging
activity was significantly enhanced [[124]67]. These property changes
may have implications for the treatment of colitis and cognitive
impairment.
3.5. Glucose and Cholesterol Adsorption Capacity
Research has shown that there is a strong link between blood sugar
levels and cognitive function [[125]68]. SDFM had a greater adsorption
ability on glucose than SDF, and this difference became more noticeable
with the increase in concentration ([126]Figure 4A). By increasing the
glucose adsorption ability, SDFM reduces the intestinal absorption of
glucose, which may help control the blood glucose levels and maintain
stable blood glucose levels, which are necessary for cognitive
function. There are numerous studies which have shown that dietary
fiber lowers blood cholesterol [[127]69]. Different mechanisms underlie
soluble dietary fiber’s ability to decrease cholesterol. The binding of
water in the food and the resulting increase in viscosity are assumed
to be the major effect [[128]70]. Some other studies have demonstrated
that there is a directly binding force between SDF and cholesterol,
which reduces the risk of cardiovascular disease [[129]71]. From our
findings, soluble dietary fiber significantly increased the cholesterol
absorption capacity at pH 7 compared to pH 2, which corresponded to
oral digestive, upper small intestinal, oral digestive, and gastric
digestive conditions, respectively. Furthermore, the cholesterol
absorption capacity of SDFM was significantly higher than that of SDF
([130]Figure 4B). SDFs adsorb cholesterol from the intestines and
reduce its absorption into the bloodstream, thus helping to lower blood
cholesterol levels. This may be beneficial in preventing cardiovascular
disease, which studies have shown to be associated with cognitive
impairment [[131]72], which may explain why SDFs can alleviate
cognitive impairment.
Figure 4.
[132]Figure 4
[133]Open in a new tab
The structural characterizations and functional properties of SDFs. The
glucose (A) and cholesterol (B) adsorption capacity of SDF and SDFM;
(C–E) The flow behavior characteristic of SDF and SDFM, including
static rheology of SDFs (C) and the dynamic rheology of SDF (D) and
SDFM (E). The data are displayed as mean ± SEM. Statical analyses were
carried out using one-way ANOVA along with Duncan’s multiple range
test. Different letters indicate significant differences (p < 0.05).
3.6. The Rheological Characteristic
3.6.1. The Steady State Rheology of SDFs
The rheological properties of SDF and SDFM are characterized under
stress, which induce changes in flow behavior and structure.
[134]Figure 4C shows the steady-state rheological properties of SDF and
SDFM at 40 mg/mL. The results show that SDFM has a lower viscosity,
which is due to the difference in monosaccharide composition. Based on
the previous results of monosaccharide composition, it can be seen that
the structure of each monosaccharide composition of SDFM changed after
cellulase treatment, and the viscous sugars have a significant effect
on the static rheology of soluble dietary fiber. Within a shear rate of
0–100 s^−1, the SDF and SDFM viscosities vary inversely to the change
in shear rate, which showed the characteristics of a pseudoplastic
fluid. When the shear rate increases, the molecules of SDF and SDFM
tend to be orientated in a directional arrangement, the interaction
between molecules is weakened, the solution mobility increases and the
viscosity decreases. It can be expected from the trend in the figure
that, after more than 100 s^−1, the SDF solution will exhibit Newtonian
fluid behavior, which suggests that the viscosity does not change with
the shear rate, probably because the high shear rearranges the
molecular skeleton of SDF and SDFM, making it difficult for the small
particles of the SDF and the SDFM to be entangled with each other,
which is reflected in the reduction in viscosity [[135]73].
3.6.2. The Dynamic Rheology of SDFs
The dynamic viscoelastic properties of SDF and SDFM samples at 10 mg/mL
concentration at angular frequencies from 0.1 to 100 rad/s are shown in
[136]Figure 4D,E. The elastic modulus G′ indicates the ability of the
material to undergo elasticity, while the viscous modulus G” indicates
the viscous characteristics of the material [[137]74]. Both G′ and G″
of SDF and SDFM increase with the angular frequency, and when the
angular frequency of SDF < 2, G′ always < G″, which indicates that SDF
behaves predominantly viscous in solution. This phenomenon is reversed
when the angular frequency > 2, and the SDF is mainly characterized by
elasticity in solution, nevertheless, the SDFM is predominantly elastic
only at an angular frequency > 5. At small angular frequencies, the G′
and G″ are close to each other, while at large angular frequencies, G′
increases exponentially while G″ increases slowly in comparison. And
the SDFM solutions exhibit lower elasticity compared to SDF solutions,
and this difference in dynamic rheological properties between fibers is
related to factors such as fiber structure, molecular chain size, and
the monosaccharide composition.
Rheological test results show that SDFM has lower viscosity and
elasticity, which makes SDFM more easily dispersed in the gut, helping
to prolong the retention time of food in the gut [[138]42]. And, lower
viscosity and elasticity make SDFM more readily and fermented by
bacteria, thus promoting the growth of beneficial bacteria such as
Akkermansia and the production of SCFAs [[139]75]. Meanwhile, low
viscosity soluble dietary fibers usually have high water holding and
swelling properties, which can absorb large amounts of water in the
intestine, increase fecal volume, and promote intestinal peristalsis
[[140]76].
3.7. Effects of SDFs on DSS-Induced Anxiety and Depression-like Behavior in
Mice
Structural characterizations (scanning electron microscopy, Fourier
transform infrared spectroscopy, particle size, X-ray diffraction,
adsorption capacity, flow behavior) confirmed that cellulase-modified
soluble dietary fiber (SDFM) exhibited better physicochemical
properties than SDF: more porous microstructure; similar polysaccharide
structure; more stable particle size distribution; higher
crystallinity; better adsorption capacity; and lower viscosity.
Therefore, we chose SDFM for further study, and examined the
anxiolytic/antidepressant effects of LSDF (500 mg/kg) and HSDF (1000
mg/kg), we carried out several behavioral tests to assess the effects
of SDFs. The specific trajectories of the mice in the OFT behavioral
experiment are shown in [141]Figure 5A. Then, the data were
statistically analyzed. As shown in [142]Figure 5B–D, compared to CON
group, DSS treatment significantly reduced the total distance, time in
the central area and movement velocity. However, compared with the DSS
group, the total distance and movement velocity in the open field test
(OFT) of LSDF group significantly increased (p < 0.05), while there was
no significance between HSDF group and DSS group (p > 0.05). For a new
object recognition test (NORT), the specific trajectories of the mice
in the NORT behavioral experiment are shown in [143]Figure 5E. And as
shown in the [144]Figure 5F–H, the moving distance and discrimination
index of LSDF group were significantly higher than DSS group (p <
0.05), but there was no significance between the HSDF group and DSS
group (p > 0.05).
Figure 5.
[145]Figure 5
[146]Open in a new tab
SDF intervention impacts cognitive behaviors following DSS-induced IBD
mice. (A) Mouse trajectories in the open field test (OFT); (B–D) The
moving distance, the time in the central area, and the movement
velocity of the OFT test; (E) Mouse trajectories in new object
recognition test (NORT); (F–H) The moving distance, time to explore
novel object and discrimination index of the NORT test. Data are
presented as mean ± SEM (n = 6). Statical analyses were carried out
using one-way ANOVA along with Duncan’s multiple range test. Different
letters indicate significant differences (p < 0.05).
The specific trajectories of the mice in the elevated plus maze test
(EPMT) and the Y-Maze Test (YMT) are shown in [147]Figure 6A,E. In EPMT
and YMT, the DSS group mice spent less time in the open arms, and made
less distance and entries in the open arms or the new arms. However,
the addition of LSDF significantly increased the time, distance and
entries in the open arm ([148]Figure 6B–D, p < 0.05), and the time and
distance in the new arms of Y-Maze Test (YMT) were significantly
increased in the LSDF and HSDF groups ([149]Figure 6F,G, p < 0.05).
And, there was generally no significant difference between the DSS
group and the HSDF group in the time and entries in the open arms of
EPMT and entries to the new arms in the YMT. Importantly, all these
tests are widely used for screening the efficacy of repairing cognition
in the DSS model of cognitive impairment [[150]77]. According to the
results above, it shows that LSDF is highly effective in DSS-induced
cognitive impairment in mice, and 500 mg/kg SDF is more effective than
1000 mg/kg SDF at anti-depression.
Figure 6.
[151]Figure 6
[152]Open in a new tab
SDF intervention impacts cognitive behaviors following DSS-induced IBD
mice. (A) Mouse trajectories in an elevated plus maze test (EPMT);
(B–D) For the EPMT test, results including the distance, time, and
entries in the open arms; (E) Mouse trajectories in the Y-Maze Test
(YMT); (F–H) For the YMT test, results including distance, time,
entries in the new arms. Data are presented as mean ± SEM (n = 6).
Statical analyses were carried out using one-way ANOVA along with
Duncan’s multiple range test. Different letters indicate significant
differences (p < 0.05).
3.8. Effects of SDFs on the Disease Parameters of DSS-Induced IBD Mice
When combined with the earlier structural characterization of SDFs,
cellulase-treated SDFMs exhibit reduced viscosity and elasticity,
increased crystallinity and adsorption, and other characteristics that
allow SDFMs to exert their effectiveness in biological organisms more
effectively than SDFs. Thus, we proceeded to examine how SDFM affected
the mice’s IBD and cognitive decline brought on by DSS. In comparison
to mice in the CON and SDFs groups, the body weight of DSS-induced IBD
mice decreased considerably throughout the course of 7 days of DSS
administration. On day 14, the body weight of mice with induced IBD
decreased by 15.00 ± 2.96% compared to the end of acclimatization
feeding on day 7. However, this impact was significantly reversed after
the SDF intervention. The body weights of mice in the low-dose
treatment group were more similar with the CON group compared to the
high-dose SDF group ([153]Figure 7A,B). DAI scores and colon length
were utilized to evaluate the effectiveness of SDFs in protecting
animals from DSS-generated colitis. DSS-induced inflammation in the
colon of mice was often accompanied by a significantly shorter colon
length compared to health mice [[154]78]. After 7 days of DSS
induction, the DAI scores of mice were noticeably greater than those of
the CON group, with a maximum score of 2.67 ± 0.27 ([155]Figure 7C).
However, SDFs therapy was able to prevent the increase in DAI scores
within 7 days. On day 14, DAI scores were significantly lower in the
LSDF group (0.72 ± 0.12). According to the study’s findings, the DSS
group’s colon length was noticeably less than that of the CON group
(7.86 ± 0.20 vs. 5.92 ± 0.14 cm; p < 0.05), as shown in [156]Figure
7D,E. After treatment with SDFs, the LSDF and HSDF group colon lengths
were significantly longer compared with the DSS group (6.95 ± 0.19 vs.
6.28 ± 0.38 cm; p < 0.05), with the LSDF group of mice having
significantly longer colon lengths.
Figure 7.
[157]Figure 7
[158]Open in a new tab
SDFs’ anti-inflammatory properties in vivo. (A) The change in body
weights in different groups; (B) The body weight gain of different
groups (the body weight of 14th day compared to 7th day); (C) Disease
activity index (DAI) scores, significance analyses were performed for
each group on the same day; (D) Images of mouse colons in different
groups; (E) Colon length of different groups. Data are presented as
mean ± SEM (n = 6). Statical analyses were carried out using one-way
ANOVA along with Duncan’s multiple range test. Different letters
indicate significant differences (p < 0.05).
The observations described previously showed that the therapeutic
effects of LSDF was superior to HSDF. Furthermore, the present study
was carried out using H&E staining to examine colonic and brain tissue
sections for a wider examination to assess the effectiveness of LSDF
and HSDF interventions ([159]Figure 8A,B). As shown in [160]Figure 8A,
for the CON group, colonic sections indicated an intact structure with
neatly arranged glands and crypts. However, there was a notable absence
of cup cells, inflammatory cell infiltration, and severe mucosal ulcers
in the colon sections of the DSS group. The SDF group showed a
reduction in pathological changes caused by DSS, and in particular, the
colonic structure of the LSDF group was more similar to the CON group
than HSDF, indicating that LSDF had potent anti-inflammatory properties
and was effective in reducing colitis in mice. Depression has been
reported to be linked with inflammatory processes
[[161]79,[162]80,[163]81]. The histological alteration in the
hippocampus was confirmed by the H&E staining of the brain. [164]Figure
8B shows that nuclear shrinkage, nuclei damage and neuronal
degeneration were increased in DSS-treated mice compared to the CON
group. Supplementation with SDFs, particularly LSDF, had good relief
for inflammation of the colon and brain.
Figure 8.
[165]Figure 8
[166]Open in a new tab
H&E-stained histopathological sections of colonic tissues, red arrow
symbols indicate the severe deformation of the colonic villous
structure, including the absence of cup cells and severe inflammatory
cell infiltration phenomenon. Scale bars: 100 μm (A) and H&E-stained
histopathological sections of brain tissues, red circles indicate
neuronal degeneration and damaged nuclei or nuclear shrinkage. Scale
bars: 50 μm (B).
3.9. Effect of SDFs on DSS-Induced Inflammation of the Gut and the Cerebral
Cortex
The depression is commonly associated with abnormal changes in the
level of inflammation. Our study found a significant reduction in serum
levels of LPS after LSDF intervention ([167]Figure 9A). Notably, LSDF
treatment also significantly increased brain-derived neurotrophic
factor (BNDF) levels of cerebral cortex (p < 0.05), while high doses of
SDF showed almost identical levels of BDNF compared with the DSS group
([168]Figure 9B,G). Consequently, we examined the pro-inflammatory
cytokines to assess the degree of inflammation in the colon and brain
tissues and found that mRNA levels of pro-inflammatory cytokines
(TNF-α, IL-6, IL-1 β, Tlr4, and NF-kB) were significantly decreased in
colon and brain tissues after the LSDF and HSDF administration
([169]Figure 9C–E,H–L, p < 0.05), while significantly increasing the
mRNA levels of IL-10 in colon ([170]Figure 9F, p < 0.05) and brain
([171]Figure 9M, p < 0.05). Overall, there was no significant
difference between the LSDF and HSDF on colon inflammation modulation,
but HSDF had a better reduction in inflammation in the brain.
Figure 9.
[172]Figure 9
[173]Open in a new tab
The inflammatory indicator level of the colon and brain: (A) Serum LPS;
(B) Serum BDNF; (C–F) Relative mRNA expression of TNF-α, IL-6, IL-1β,
and IL-10 in the colon; (G–M) Relative mRNA expression of BDNF, Tlr4,
NF-κB, TNF-α, IL-1β, IL-6, and IL-10 in the brain. Data are presented
as the mean ± SEM (n = 6). Statical analyses were carried out using
one-way ANOVA along with Duncan’s multiple range test. Different
letters indicate significant differences (p < 0.05).
3.10. Effect of LSDF on DSS-Induced Changes in Gut Microbiological
Composition
In summary, for behavioral experiments (open field test (OFT), new
object recognition test (NORT) and elevated plus maze test (EPMT)),
LSDF showed a better alleviation of cognitive deficits than HSDF. The
DSS-induced body weight of IBD mice showed significant weight loss, but
the effect of weight loss after LSDF intervention was significantly
reversed to a greater extent than that of HSDF. The LSDF group had a
significantly lower DAI score than the HSDF group and had a longer
colon length. Colon and brain tissue sections were assessed by H&E
staining, LSDF group had a more similar colon and cerebral cortex
structure to the CON group compared to the HSDF group. Biochemical
analyses showed that LSDF was more effective than HSDF in decreasing
the serum LPS levels and increasing the BDNF levels in the cerebral
cortex, which may contribute to the improvement in colitis and
cognitive impairment induced by DSS. Interestingly, HSDF performed
better in reducing brain inflammation. Summarizing these previous
findings, LSDF had overall better efficacy than HSDF.
Gut microbiota plays an important role in maintaining gut barrier
function, and its imbalance is recognized as a key factor in UC and
cognitive impairment. We therefore further verified the alleviating
effect of LSDF by investigating its modulating effect on gut
microbiota. The 16S rRNA sequencing of cecum contents was conducted to
validate the effect of LSDF intervention on the intestinal microbiota
of mice with colitis. The α-diversity indices showed that the Chao 1
index and observed-species index of species richness in the DSS group
mice significantly decreased compared to the CON group mice, while the
trend was reversed after LSDF intervention (p < 0.05). Although the
Shannon indices of LSDF mice showed certain increases compared to CON
and DSS mice, there was no statistically significant difference. GM in
the LSDF group was significantly improved by LSDF compared with those
in the DSS group ([174]Figure 10A).
Figure 10.
[175]Figure 10
[176]Open in a new tab
Effect of LSDF on the species diversity of the gut microbiota in DSS
mice. (A) OTU level analysis in three groups, including chaos index,
observed species, and Shannon index; β-diversity indices including PCOA
(B) and NMDS (C). Data are presented as mean ± SEM (n = 6). Statical
analyses were conducted using one-way ANOVA analysis, * p < 0.05, ** p
< 0.01.
β-diversity reflects differences in the species abundance and
distribution between communities, and we analyzed β-diversity indices
such as PCoA ([177]Figure 10B) and NMDS ([178]Figure 10C) between
different groups. PCoA reveals the distinct clustering of the gut
microbiota composition among the CON, DSS, and LSDF groups, which
indicated that LSDF supplementation significantly changed the gut
microbial populations. Thereafter, these changes among different groups
were confirmed by the NMDS analysis. The significant intergroup
variability between mice in the CON, DSS, and LSDF groups suggested the
significant changes in the fecal microbiota of mice in the DSS-induced
colitis disease state. The LSDF intervention deviated from the model
group to a certain extent and converged to the CON group. These results
indicate that, overall, there were significant differences in the gut
microbial structure of mice in the CON and DSS groups, while the LSDF
group converged toward the CON group.
To evaluate the effect of LSDF intervention on the structure of
intestinal microbiota, we compared the relative abundance of the gut
bacteria in the CON, DSS, and LSDF groups of mice with colitis. At the
phylum level, 10 primary phyla were defined in our study, DSS
significantly increase the relative abundance of Bacteroidetes and
decreased that of Firmicutes, Verrucomicrobia, and Deferribacteres,
while the intervention of LSDF robustly increased the abundance of
Firmicutes, Proteobacteria, Verrucomicroia, and Deferribacteres and
decreased the ratio of Bacteroidetes ([179]Figure 11A). At the genus
level, the abundances of Akkermansia, Allobaculum, Aggregatibacter,
Helicobacter in the cecum contents of mice were increased, while the
abundances of Bacteroides, Clostridium, Flexispira, Escherichia, and
Anaerotruncus were decreased after the LSDF intervention compared to
the DSS group ([180]Figure 11B). Overall, the DSS altered the
composition of the intestinal flora and promoted the growth of harmful
bacteria (Flexispira, Bacteroides, and Escherichia, etc.), whereas the
LSDF intervention promoted the growth of beneficial bacteria
(Helicobacter and Aggregatibacter, etc.).
Figure 11.
[181]Figure 11
[182]Open in a new tab
The effect of LSDF on the composition of the gut microbiota in mice.
(A) The phylum-level relative abundance histogram of gut microbiota in
three groups; (B) The genus-level relative abundance histogram of gut
microbiota in three groups.
Next, to define the characteristic bacteria of three groups, LEFSe
analysis was performed from the phylum to genus levels with an LDA
threshold of 2 ([183]Figure 12A,B). The results indicated that a total
of 50 OTUs were notably different among these three groups, among which
there are 20, 13, and 17 significant differences in the CON, DSS, and
LSDF groups, respectively. The mice in the CON group had 20 biomarkers,
3 of which were part of the phylum Firmicutes, 6 of which were part of
the phylum Bacteroidetes, 4 of which were part of the phylum
Proteobacteria, 4 of which were part of the phylum TM7, 2 of which were
part of the phylum Cyanobacteria, and 1 of which were part of the
phylum Actinobacteria. DSS mice had 13 biomarkers, 8 of which were part
of the Proteobacteria, 3 of which were part of the phylum Firmicutes,
and 2 of which were part of the phylum Bacteroidetes. LSDF mice had 17
biomarkers, 9 of which were part of the phylum Proteobacteria, 6 of
which were part of the phylum Deferribacteres, and 2 of which were part
of the Firmicutes. Particularly, o_Enterobacteriales, g_Bacteroides,
and g_Flexispira were found to play an important role in the DSS
treatment group; c_Gammaprotebacteria, o_Pasteurellaceae, and
g_Aggregatibacter were found to play an important role in the
supplementation of LSDF group under DSS condition. The results showed
that there were significant differences in the community structure
between the different groups, especially in the DSS group compared to
the CON and LSDF groups, while the LSDF group was closer to the CON
group, and the findings in [184]Figure 12A are consistent with those in
[185]Figure 12B.
Figure 12.
[186]Figure 12
[187]Open in a new tab
Effect of LSDF on the differentially abundant microbial composition of
the gut microbiota in mice. (A) Scores for the abundances of different
taxa using linear discriminant analysis (LDA). (B) Taxonomic cladogram
obtained using LEfSe analysis.
Overall, these findings imply that LSDF has a role in influencing gut
microbial composition and altering its overall function.
3.11. Effect of SDFs on DSS-Induced Changes in Fecal SCFAs Levels
Research has indicated that an imbalance in the gut microbiota may
result from colitis [[188]82]. SCFAs are crucial for retaining the
morphology of colonic epithelial cells, which is therefore utilized to
effectively prevent intestinal diseases [[189]83,[190]84]. In this
study, we used the gas chromatography to ascertain the short-chain
fatty acid composition. The results indicated that the contents of
SCFAs, including acetic acid, propionic acid, butyric acid, iso-butyric
acid, valeric acid, and iso-valeric acid were low in the DSS group, and
there was no obvious enhancement after LSDF treatment, except for
iso-butyric acid ([191]Figure S1). Furthermore, it was found that high
dosages of soluble dietary fiber had a stronger intervention effect for
acetic and propionic acids. In summary, the results showed that LSDF
and HSDF promoted the production of SCFAs.
3.12. LSDF Altered the Metabolic Profile of DSS-Induced Mice
To determine the mechanisms and pathways by which LSDF alleviates the
symptoms of colitis-induced cognitive impairments, we conducted brain
metabolomics experiments. We selected the LSDF group for metabolomics
analysis and further discussion after comparing the intervention
effects of LSDF and HSDF on the moderating impact of colitis and
discovering that the effect of the LSDF was more substantial than HSDF.
As a popular research approach in recent years, metabolomics can help
us discover certain relevant biomarkers and gain a better understanding
of the pathological processes and metabolic pathways of substances in
the body. To explore whether LSDF ameliorates DSS-induced colitis
through its metabolites, cerebral cortex tissues from the DSS and LSDF
groups were collected for non-targeted metabolomics analysis. From the
scores of multidimensional statistical analysis, including PCA
([192]Figure 13A) and OPLS-DA ([193]Figure 13B), the result showed that
the loading plots of the LSDF group are clearly distinguishable from
the DSS group. This indicates that, compared to the DSS group, the LSDF
had a significant impact on brain metabolite composition. For the
OPLS-DA test, when the number of permutation tests was 200, R2 = 0.586
and Q2 = −1 were obtained, which indicated that all OPLS-DA models were
reliable without overfitting ([194]Figure 13C). The results of the
volcano map revealed significant alterations in brain metabolites after
intervention with the LSDF, among which 294 were upregulated and 311
were downregulated ([195]Figure 13D).
Figure 13.
[196]Figure 13
[197]Open in a new tab
Effect of LSDF on brain metabolites in mice. (A) PCA based on mouse
brain metabolites; (B) OPLS-DA based on mouse brain metabolites; (C)
Diagram of a permutation test using the OPLS-DA method, where the
number of tests was 200; (D) Metabolite volcano plots for the DSS and
LSDF group; and (E) The metabolic pathways of brain differential
metabolites between DSS and LSDF group.
In this study, based on the OPLS-DA and t-test results, fold change ≥ 2
or fold change ≤ 0.05, VIP value > 1, and p-value < 0.05 were set as
the differential metabolites screening conditions. To more intuitively
demonstrate the pathways that the differential metabolites were
enriched into between the DSS group and LSDF group, the KEGG pathways
of the differential metabolites was mapped. The analysis of the KEGG
pathway in the LSDF group vs. the DSS group revealed that the
differentially abundant metabolites of the cerebral cortex identified
herein were mainly involved in the sphingolipid metabolism, linoleic
acid metabolism, glycerophospholipid metabolism, and so forth
([198]Figure 13E).
The mechanistic diagram of the sphingolipid metabolic pathway was shown
in [199]Figure 14A. In the mouse brain of LSDF group, significant
declines were observed in the levels of certain small metabolites
linked to sphingolipid metabolism pathways, such as sphingomyelin (SM),
ceramide (SER), sphingosine (SPH), and sphingosine-1-P (S1P) compared
to the DSS group ([200]Figure 14B–E). Then, we used RT-qPCR to assess
the expression levels of genes linked to the sphingolipid metabolism in
order to validate the findings of metabolomics on the enrichment of
metabolite pathways ([201]Figure 14F–K). The intervention of LSDF may
cause changes in the transcription factor levels associated with
sphingolipid metabolism. Results indicated that the LSDF intervention
significantly upregulated the expression of Sphingomyelin synthase 1
(SGMS1), Ceramide synthase 4 (CERS4), Ceramide synthase 6 (CERS6),
Sphingosine kinase 1 (SPHK1), and Sphingosine kinase 2 (SPHK2) in the
cerebral cortex, while significantly downregulating the expression of
Sphingomyelin synthase 1 (SGMS2). This finding further demonstrated
that the treatment with LSDF restored the imbalance in the sphingolipid
metabolism caused by DSS.
Figure 14.
[202]Figure 14
[203]Open in a new tab
Impact of LSDF supplementation on the sphingolipid pathway of the brain
in mice. (A) Mechanistic diagram of sphingolipid metabolic pathway
(downward arrows indicate decreases in levels of relevant metabolites);
(B–E) Normalized relative abundance of metabolites related to
sphingolipid metabolic pathway: sphingomyelin (SM), ceramide (CER),
sphingosine (SPH), and sphingosine-1P (S1P)—statistical analyses were
carried out using two-tailed t-tests with Student’s t-tests; (F–K) mRNA
expression of important genes in the sphingolipid metabolic pathway of
brain: SGMS1, SGMS2, CERS4, CERS6, SPHK1, and SPHK2. Data are presented
as mean ± SEM (n = 6). Different letters indicate significant
differences (p < 0.05), ** p < 0.01, *** p < 0.001.
3.13. Spearman Correlation Analysis Between Biochemical Indices, Behavioral
Parameters, Brain Metabolites, and the Microbiota
Based on the correlation of various parameters, Spearman correlation
was used to validate the potential relationships between various
parameters, including gut and brain inflammatory factors, behavioral
experiments, brain sphingolipid metabolism and intestinal microbiota
([204]Figure 15). In mice, Flexispira, Bacteroides, and Escherichia
exhibited positive correlations with the levels of the sphingolipid
metabolite (SM, ceramide, SPH, and S1P), the expression of mRNA SGMS2,
inflammation factors of gut and brain (LPS, TLR4, IL-1β, TNF-α, IL-6,
and TNF-α). On the other hand, it was negatively correlated with the
expression of the sphingolipid pathway-related mRNA (SGMS1, CERS4,
CERS6, SPHK1, and SPHK2), BDNF, anti-inflammatory IL-10, and cognitive
behavior (OFT, NORT, EPM, YM). Interestingly, the Helicobacter and
Aggregatibacter showed opposite results in contrast to the Flexispira,
Bacteroides, and Escherichia.
Figure 15.
[205]Figure 15
[206]Open in a new tab
Spearman correlation between genus-level microflora and inflammatory
indices of colon and brain, cognitive behavior, and the sphingolipid
metabolism of brain. Statical analyses were conducted using t-test, * p
< 0.05, ** p < 0.01, *** p < 0.001.
4. Discussion
In the presented study, SDFs’ effects on DSS-induced colitis in mice
and the accompanying behaviors of anxiety and depression have been
assessed. It was found that the SDFs reduced the damage to the
intestinal barrier. This effect’s mechanism may be related to the
inhibition of inflammatory responses, which is in line with the results
on inflammatory cytokines (TNF-α, IL-6, and IL-1β) in the brain and
colon. Additionally, SDFs reversed some DSS-induced alterations in gut
microbiota metabolites, including LPS and SCFAs. Moreover, SDFs
remodeled the composition of the gut microbiome, and the abundance of
beneficial bacteria was upregulated, and that of harmful bacteria was
downregulated in DSS-induced IBD mice. At the same time, SDFs were
found to improve synaptic plasticity in the brain by modulating the
level of BDNF and sphingolipid-related metabolite, thereby preventing
behavioral disorders, which suggests that gut–brain axis homeostasis
may also be the underlying mechanism.
In recent years, the incidence of colitis has been increasing,
accompanied by a greater probability that the patient will suffer from
a psychiatric disorder, and it has been challenging to develop
medications due to the intricate etiology. IBD and its accompanying
anxiety and depression are among the gastrointestinal symptoms that are
attributed to behavioral disorders by the gut–brain axis [[207]85].
Evidence from both humans and rodents indicates the connection between
intestinal barrier disruption and depression and anxiety. This damage
may result in the release of some pathogens, including LPS, into the
plasma, which may trigger neuroinflammation [[208]86,[209]87]. For
instance, intestinal permeability is increased and serum LPS (the main
external membrane constituent of Gram-negative bacteria) is increased
by 3-fold in dementia patients with endotoxemia. According to the
current studies, there is an increasing amount of evidence from sizable
cohorts suggesting a connection between depressive or anxiety symptoms
and the IBD clinical disease activity [[210]88,[211]89]. The prevalence
of psychiatric disorders is also higher in patients with IBD: almost
half of them experience anxiety symptoms, and one-third experience
depressive symptoms [[212]90]. More recent studies in rodents have
shown that DSS-induced depressive-like and anxiety-like behaviors
correlate with gut microbiota composition, apoptosis, synaptic damage,
neuroinflammation, as well as BDNF levels [[213]91,[214]92,[215]93]. As
an important component of the gut-brain axis, gut microbiota plays an
essential for cognitive function [[216]94]. In conclusion, in
behavioral experiments (including the open field test (OFT), the new
object recognition test (NORT) and the elevated cross maze test
(EPMT)), LSDF showed a better alleviation of cognitive deficits
compared to the high dose (HSDF). And, biochemical analyses showed that
LSDF was more effective than HSDF in decreasing the serum
lipopolysaccharide (LPS) levels and increasing the brain-derived
trophic factor (BDNF) levels in the cerebral cortex, which may
contribute to the improvement in colitis and cognitive impairment
induced by DSS.
As the fifth nutrient, dietary fiber has been increasingly found to
provide important benefits to human health [[217]95]. The structural
characteristics, binding capacity, and nutrient transport capacity of
dietary fiber are relevant to the functions it plays in the digestive
tract [[218]5]. More and more research in recent years has shown that
dietary fiber can modulate cytokines [[219]96,[220]97], altering the
structure of the gut microbiota [[221]98] to alleviate intestinal
inflammation, which has enormous potential for treating IBD [[222]99].
It has also been reported that dietary fiber is more suitable for
intervention in ulcerative colitis disease than Crohn’s disease
[[223]99,[224]100]. A study revealed that celery SDF was more effective
than IDF in alleviating colitis and reducing the interfering effects of
flavonoids [[225]101]. Millet soluble dietary fiber was found to
alleviate DSS-induced colitis by increasing lactobacilli and F/B ratio
to maintain intestinal microbiota balance [[226]102]. In this study,
the DSS-induced body weight loss was significantly lower in the IBD
mice, but the recovery of body weight after the LSDF intervention was
significantly better than that of HSDF. The LSDF group had a
significantly lower disease activity index (DAI) than the HSDF group
and a longer colon length. Colonic and brain tissue sections assessed
by H&E staining showed that the LSDF group had colon structures that
were more similar to those of the control group (CON), in contrast to
the HSDF group. Taking these together, these findings suggest that LSDF
is overall more effective than HSDF in terms of relieving symptoms
associated with inflammatory conditions of the colon.
There is widespread agreement that the pathomechanism of ulcerative
colitis is the interaction of exposure to the environment in
genetically susceptible individuals, coupled with the dysbiosis of the
intestinal microbiota, epithelial barrier defects, and immune
dysregulation [[227]11]. The intestinal epithelial barrier defects
refer to the absence of cuprocytes, whereas cuprocytes secrete mucin-2
that provides a protective layer for the colon [[228]103,[229]104].
Recent studies indicated that intestinal permeability increases when
the intestinal barrier is disrupted, while the metabolism and
production of a number of pathogens, including LPS, are more likely to
enter the bloodstream through the intestinal epithelium, which can
exacerbate other inflammatory conditions, such as neuroinflammation in
vivo [[230]105,[231]106,[232]107]. The harm to the central nervous
system (CNS) is linked to genetic susceptibility and environmentally
induced shifts in the metabolite and protein levels [[233]25,[234]108].
Diets to improve cognitive impairment and prevent dementia are
currently a major research hotspot today [[235]109]. The cell wall
component lipopolysaccharide (LPS) could induce exacerbated
neuroinflammation by activating microglia, thereby inducing an increase
in neuroinflammation, which is thought to be associated with cognitive
decline [[236]110,[237]111]. In the present study, we found that SDFs
significantly downregulated LPS levels in the serum of mice (p < 0.05),
especially LSDF, which had a better effect compared to HSDF.
Brain-derived neurotrophic factor (BDNF) has been found to be effective
in enhancing the stress response triggered by Arg1 microglia in the
hippocampus, thereby effectively alleviating depression [[238]112]. In
this study, we found that BDNF levels in the serum and brain tissues of
mice with colitis were significantly increased after LSDF intervention
(p < 0.05), while there was no significant difference in the BDNF
levels after the HSDF intervention compared with the DSS group (p >
0.05), which suggests that the LSDF intervention is more effective in
increasing BDNF levels and thus alleviating depression.
Cytokines refer to a series of small heterogeneous peptides which can
affect cell proliferation and differentiation as well as inflammation,
as evidenced by studies that the study of these cytokines is important
for psychiatric disorders like depression and anxiety [[239]113], of
which IL-1β has been implicated in the etiology of depressive-like
behaviors, and in conjunction with IL-6 and TNF, affects depressive
disorders [[240]114]. Some studies have shown that IL-1β levels in
depressed patients are elevated and positively correlated with the
degree of illness in elderly depressed patients and in women with
postpartum depression [[241]115,[242]116]. Interleukin-6 is a neuronal
growth factor that has been linked to depressive symptoms such as low
mood and decreased appetite [[243]117], and several studies have
demonstrated elevated levels of IL-6 in depressed patients
[[244]118,[245]119]. TNF-α levels have been found to be positively
correlated with major depressive disorder (MDD) severity in serum and
have a predictive value [[246]120]. Studies have shown that adult
C57BL/6 mice fed high dose of pectin have decreased levels of TNF-α,
IL-1β, and IL-6 in their hippocampus while brain-derived neurotrophic
factor levels are increased, thereby reducing neuroinflammation and
affecting mood- and cognition-related brain regions [[247]121]. TLR4 is
a pattern recognition receptor that plays an important role in the
innate immune system, recognizing pathogen-associated molecular
patterns (PAMPs), such as bacterial LPS [[248]122]. The activation of
TLR4 can set off signaling pathways that lead to the development of
pro-inflammatory factors (TNF-α, IL-1β, and IL-6) that trigger
inflammation [[249]123]. NF-κB is a protein complex involved in the
expression of various genes encoding inflammatory factors and immune
response regulators, and its activation has been associated with a
variety of inflammatory diseases [[250]124]. It has been proved that
treatment with SCFAs can reduce intestinal inflammation by decreasing
NF-κB signaling pathway and upregulating the expression of the
anti-inflammatory cytokine IL-10 [[251]125]. Over all, there was no
significant difference in serum inflammation levels (TNF-α, IL-6, IL-1β
and IL-10) between the LSDF and HSDF groups. Interestingly, HSDF
performed better in reducing brain inflammation (Tlr4, NF-κB, IL-6, and
IL-10).
There is growing evidence that imbalances in the gut microbiota’s
structure are strongly linked with increased intestinal permeability,
induced intestinal inflammation, and peripheral blood inflammation,
increased blood–brain barrier permeability, and increased central
inflammation, which leads to neurological dysfunction
[[252]126,[253]127]. Studies have confirmed that the restoration of gut
microbiota structure can increase intestinal permeability and
ameliorate abnormal cognitive behavior in mice [[254]23]. At the phylum
level, it has been investigated that Firmicutes can preserve the
integrity of the intestinal barrier by upregulating the tight junction
protein and activating the Akt/MTOR signaling pathway, playing an
essential role in the mouse models of UC [[255]128]. In addition,
studies have shown that Firmicutes improved memory scores in
Alzheimer’s disease models and human subjects with self-reported memory
problems, and in a trial of healthy adults with MCI, Firmicutes also
significantly improved cognitive function [[256]129]. The changes in
gut microbes have been linked to the development of UC. Specifically,
there is research which has found that the elevated abundance of
Bacteroides in IBD patients may be related to the pathogenesis of
colitis [[257]130], which aligned with our study results. Additional
analysis was performed on the species composition at the genus level.
After DSS intervention, there were large changes in microbiota
abundance, and LSDF reversed these changes, including the abundance of
harmful bacteria like Clostridium, Flexispira, Escherichia, and
Anaerotruncus decreased, while the abundance of Allobaculum,
Aggregatibacter, Helicobacter and Akkermansia and other beneficial
bacteria increased. It was found that Escherichia increased the level
of intestinal inflammation by secreting TNF-α and IL-6 [[258]131].
Studies revealed that Akkermansia improves cognitive dysfunction by
regulating BDNF and inflammation levels in the gut-brain axis
[[259]132,[260]133]. The currently recognized possible mechanisms of
Akkermansia in the treatment of colitis are the increased production of
SCFA, promoted pro-inflammatory cytokines, and the altered composition
of the intestinal microbiota [[261]134]. In addition, another study
found that silibinin increased the level of Allobaculum and
Akkermansia, and then alleviated memory deficits in AD rats, and
decreased amyloid plaque deposits in the brain, which was consistent
with the results of our study [[262]135].
Previous research has proven that SCFAs can modulate intestinal
permeability, inhibit the release of pro-inflammatory molecules, and
ultimately alleviate ulcerative colitis [[263]136]. Our findings
indicated that the six types of SCFAs—acetic acid, propionic acid,
butyric acid, iso-butyric acid, valeric acid, and iso-valeric acid—that
are metabolites of dietary fiber digested by the microbiota were
significantly elevated under the SDFs intervention. Research has
demonstrated that SDF typically exerts beneficial effects through the
regulation of systemic energy homeostasis by SCFAs [[264]137].
Recently, results have shown that the gut microbiome can generate SCFAs
through the phylum Firmicute, Actinobacteria, and Bacteroidetes. Some
studies have found that UC patients have a lower abundance of the
butyrate-producing genera Faecalibacterium prausnitzii and Roseburia
hominis from the phylum Firmicutes [[265]138,[266]139]. The growth of
these beneficial bacteria and the production of short-chain fatty acids
(SCFAs) may be influenced by the structural features of SDFM: surfaces
with more cracks and pores may provide more attachment sites and
protection for bacteria, thus promoting the growth of beneficial
bacteria; hydrolysis by cellulase to produce oligosaccharides or
monosaccharides, which can act as fermentation substrates for gut
bacteria; the higher crystallinity of SDFM affects its solubility and
fermentability in the gut; the lower viscosity and elasticity of SDFM
may be more accessible and fermentable by bacteria, thus promoting the
growth of beneficial bacteria and the production of SCFAs.
Furthermore, in this study, the KEGG pathway analysis of brain
metabolites in mice also revealed that the sphingolipid metabolism was
the metabolic pathway with the largest pathway impact factor after LSDF
intervention. It has been found that Saikosaponin enhances the
sphingolipid metabolism in the cerebral cortex through apolipoprotein
E, which leads to neurovascular coupling and exerting its
antidepressant effects [[267]140]. Metabolomics analysis in this study
revealed that DSS-induced cognitive deficits in mice as well as
intestinal microbiota dysbiosis, which also resulted in dysregulated
sphingolipid metabolism. Consistent with our results, several clinical
trials have demonstrated that the dysregulation of the sphingolipid
metabolism is a significant aspect of dysfunction in patients with
depression [[268]141,[269]142]. Notably, under DSS, mice brain showed
lipid metabolism disorders (the level of SM, CER, SPH, and S1P in the
brain of mice increases) which implies that the pathophysiology of
cognitive impairment involves DSS-mediated sphingolipid metabolism.
After the intervention of LSDF, the abnormal sphingolipid metabolism
can be alleviated by regulating the expression level of genes related
to sphingolipid metabolic pathway (SGMS1, SGMS2, CERS4, CERS6, SPHK1,
and SPHK2), thus alleviating the dysfunction of the central nervous
system of mice and alleviating the cognitive and emotional
abnormalities. Sphingolipid is a kind of amphoteric lipid containing
sphingosine skeleton, which is an important structural component of
cell membrane containing SM, CER, SPH, and other substances, which
participates in various cellular processes, such as cell interaction,
cell proliferation, migration, differentiation, and apoptosis
[[270]143]. Sphingolipid metabolism has been reported to participate in
some pathophysiological mechanisms of depression, including
inflammation, neurodegeneration, and HPA axis activation [[271]144].
The level of SM is crucial for cell function, which is the fundamental
component of the plasma membrane [[272]145], and SM has been proposed
as a novel target for antidepressant treatment [[273]146]. In addition
to forming the structure of cell membrane, CER also plays a major role
in the physiological processes of differentiation and cell growth
[[274]147]. The sphingomyelin (SM)-ceramide (CER) pathway has been
found to be an important regulator of neurodegenerative diseases
[[275]148,[276]149]. TNF-α induces the formation of ceramides, which
mediate apoptosis. Ceramide metabolites (Sphingosine-1-P (S1P)) play an
important role in inflammation by inhibiting ceramide-mediated
apoptosis through the activation of extracellular pathways, such as
extracellular signal-regulated kinases (ERKs) [[277]150,[278]151]. In
addition, sphingosine (SPH), a by-product of ceramide commonly
associated with cellular stress response and apoptosis, can be further
phosphorylated by sphingosine kinase to produce S1P [[279]152]. The
results of this study have important implications for understanding the
antidepressant mechanism of LSDF, which may be linked to regulating the
sphingolipid metabolism to alleviate the symptoms of DSS mice.
In this study, we found that the intervention of SDFs from lentil hulls
significantly alleviated anxiety-like and depression-like behaviors in
mice with DSS-induced colitis. Moreover, SDFs exerted a prominent
effect on reducing intestinal barrier damage, serum LPS levels, and
inflammation in both the gut and brain. We also noted that the levels
of BDNF were enhanced in mice supplemented with LSDF. Notably, LSDF
demonstrated superior intervention effects compared to HSDF. The
further analysis of the gut microbiota showed a decrease in the
abundance of harmful bacteria such as Bacteroides, Clostridium,
Flexispira, Escherichia, and Anaerotruncus, and an increase in the
abundance of benefit bacteria such as Allobaculum, Aggregatibacter,
Helicobacter, and Akkermansia after LSDF intervention. Moreover, the
metabolomics results showed that LSDF affected the metabolic profile of
brain tissue mainly through the sphingolipid metabolic pathway, and the
preliminary validation of the sphingolipid pathway was obtained by
liquid–liquid chromatography and RT-PCR. Furthermore, Spearman
correlation analysis indicated that harmful bacteria (Flexispira,
Bacteroides, and Escherichia) were positively correlated with gut and
brain inflammatory factors and metabolites related to the sphingolipid
pathway, while the expression levels of genes related to the
sphingolipid pathway, the brain-derived trophic factor BDNF, the
anti-inflammatory factor IL-10, and cognitive behaviors (OFT, NORT,
EPM, YM) became negatively correlated, while the opposite results were
observed for beneficial bacteria (Helicobacter and Aggregatibacter),
supporting the beneficial role of LSDF in mitigating DSS-induced
colitis, anxiety, and depression. Overall, our study demonstrated that
LSDF exerts anti-inflammatory and antidepressant effects in DSS-induced
IBD mice by modifying the structural composition of the intestinal
bacteria and brain sphingolipid metabolism. However, there were
differences between the DSS model and human IBD [[280]153,[281]154],
although DSS-induced colitis is the most popular and flexible model for
preclinical IBD studies, it is not an exact replica of human colitis
and some of the results obtained from this model cannot be directly
applied to humans [[282]155]. In addition, the association of DSS with
SCFAs in the lumen of the colon or sphingolipid metabolites in the
brain may be quite different from the gut–brain axis of humans. Further
subsequent studies in human subjects are needed to confirm these
effects.
5. Conclusions
This study evaluated the effects of lentil hull soluble dietary fibers
(SDFs) on DSS-induced colitis and associated anxiety and depression in
mice. Structural characterizations revealed that cellulase-modified
soluble dietary fiber (SDFM) had a more porous microstructure, a more
stable particle size distribution, higher crystallinity, better
adsorption capacity, and lower viscosity compared to soluble dietary
fiber (SDF). Then, we chose SDFM for animal experiments. The results
showed that SDFM significantly reduced the anxiety-like behaviors,
intestinal barrier damage, and inflammation in both the gut and brain.
LSDF outperformed HSDF in improving cognitive deficits, reversing
weight loss, lowering DAI scores, and restoring colon length and tissue
structure, with better effects on serum LPS and BDNF levels. Further
study showed that LSDF modulated gut microbiota by decreasing harmful
bacteria (e.g., Bacteroides, Escherichia) and increasing beneficial
species (e.g., Akkermansia, Helicobacter). Metabolomics analysis
revealed that LSDF altered brain metabolism primarily via the
sphingolipid pathway. At last, the Spearman correlation analysis
indicated that beneficial bacteria were negatively correlated with
inflammatory markers and sphingolipid metabolites, while harmful
bacteria showed positive correlations. These findings provide novel
insights into the mechanism by which soluble dietary fiber maintains
the balance of the gut–brain axis and highlights the potential benefit
of lentil hull soluble dietary fiber in both the prevention and therapy
of colitis and its neuropsychiatric complications. Future studies can
consider the inclusion of female mice to better understand potential
sex-specific mechanisms underlying the relationship between colitis and
cognitive function. Also, future clinical trials are needed to confirm
the efficacy and safety of SDF in humans with colitis and cognitive
impairment, providing the basis for investigating the therapeutic
potential of SDF in human IBD.
Acknowledgments