Introduction

Depression is a complex and multifactorial mental disorder characterized by low mood1. Its clinical manifestations include cognitive slowing, anhedonia, loss of appetite, and sleep disorders2. In severe cases, individuals may experience self-harm and suicidal ideation3. With the intensifying social competition, people’s pace of life has accelerated and they are increasingly experiencing long-term disorder. As a result, mental pressure continues to mount, leading to a yearly increase in the incidence of depression. This trend has placed heavy psychological, spiritual, and economic burdens on both society and families4. According to the World Health Organization, depression affects more than 350 million people worldwide, making it the second most prevalent human disease after coronary heart disease5. Currently, the primary approach to treating depression is medication-based. However, at least 50% of patients are poor responders to antidepressants6, and there are drawbacks such as dependence and numerous adverse reactions7.

The pathogenesis of depression remains incompletely understood. Comprehensive literature analyses have demonstrated that the disruption of gut microbiota is closely associated with the pathophysiology of depression8,9,10. For example, inflammation resulting from disruptions in the gut flora and the integrity of the intestinal barrier is considered to be one of the mechanisms underlying depression11, with the microbiota-gut-brain axis playing a crucial role in this process12. When the body is exposed to external stressors, damage to the intestinal epithelium occurs, leading to increased permeability. This allows gram-negative bacteria carrying lipopolysaccharide (LPS) to translocate, thereby promoting the release of pro-inflammatory cytokines13. Additionally, metabolites produced by gut microbes, such as tryptophan, indoles, and LPS, may contribute to the development of depression through immune and inflammatory pathways or by affecting gut and blood–brain barrier permeability14. Clinical studies have revealed significant disparities in the diversity and composition of gut microbiota between individuals with depression and those who are healthy15. Furthermore, following the transplantation of gut microbiota from individuals with depression, the germ-free mice exhibited behaviors resembling those associated with depression and anxiety16.

Lactoferrin (LF) is a nutritional fortifier that plays a role in regulating intestinal flora and possesses anti-inflammatory properties17. Animal studies have demonstrated that lactoferrin intervention in depressed mice can lead to a significant reduction in immobility time during the forced swimming test18. Izumo discovered that LF enhanced the release of dopamine and 5-hydroxytryptamine from the amygdala, leading to an increase in voluntary activity in depressed rats19. Additionally, Wang et al.’s study demonstrated that the absence of LF feeding during lactation in mice disrupts the intestinal flora and influences the intestinal barrier, which elevates the risk of depressive-like behaviors when mice are exposed to external stimuli in adulthood20. Clinical studies have demonstrated that LF has the capacity to increase neurotrophic factors, improve cognition, and provide neuroprotection21,22. The findings of the aforementioned scholars consistently indicate that LF demonstrates antidepressant-like effects. However, further investigation is necessary to elucidate its mechanism of action.

Research indicates that the disruption of synaptic plasticity in the hippocampus, a region of the brain associated with mood and a major target of stress mediators, may play a crucial role in the pathophysiology of depression23. The chronic unpredictable mild stress (CUMS) model is utilized to induce depressive symptoms in animals by replicating adverse stressful events experienced in human life. The pathophysiological mechanism of this model closely resembles the real pathogenesis of human depression24. Social isolation has a profound impact on mental health across all stages of life and is a significant contributing factor to the development of depression25. Therefore, the present study utilized individually housed combined with CUMS to establish a rat model of depression. The methods of 16S rDNA sequencing and LC–MS untargeted metabolomics analysis will be employed to investigate the impact of LF on the intestinal microbiota and hippocampal tissue metabolites in depressed rats.

Materials and methods

Materials

Animals

Twenty-four 7-week-old male Sprague–Dawley rats, weighing 170 ± 10 g and with a specific pathogen-free (SPF) status, were obtained from Beijing Spefo Co. (License number: SCXK(Beijing) 2019–0010). All animals were housed in the Experimental Animal Centre of Inner Mongolia Medical University. The experimental conditions were maintained as follows: ambient temperature at 25 ± 1 ℃, relative humidity at 60%, and a light/dark cycle of 12 h each. All experiments were approved by the Animal Experimentation Committee of Inner Mongolia Medical University. The study has been conducted in compliance with the ARRIVE guidelines (https://arriveguidelines.org).

Reagents and drugs

Lactoferrin (LF, TK20230311-1) was obtained from Shaanxi Taike Biotech Co., Ltd. The Rat IL-6 ELISA Kit (MM-0190R1), Rat TNF-α ELISA Kit (MM-0180R1), Rat IL-1β ELISA Kit (MM-0047R1), Rat LPS ELISA Kit (MM-0647R1), and Rat DAO ELISA Kit (MM-21169R1) were purchased from Jiangsu Meimian Industrial Co., Ltd. Methanol. Acetonitrile was procured from Merck & Co Inc. Ammonium formate solution, ammonia solution, formate (F112034), and acetate (A298827) were procured from Shanghai Aladdin Biochemical Technology Co., Ltd. The ZO-1 antibody (PB9234), SABC Immunohistochemical Staining Kit (SA1022), PBS phosphate buffer, and citrate antigen repair solution were purchased from Wuhan Boster Biological Technology Co., Ltd. 4% paraformaldehyde fixative (P1110) and Hematoxylin-Eosin (HE) Stain Kit (G1120) were purchased from Beijing Solarbio Science & Technology Co., Ltd.

Methods

Experimental design

Based on the findings of our group’s pre-experiment and relevant literature26,27, we utilized 2% LF (2 g/100 ml) as an intervention for a duration of 44 days in this study. After a 7-day acclimatization period, a total of 24 7-week-old male Sprague–Dawley rats were randomly assigned to one of three groups: a control group (K, n = 8), a model group (M, n = 8), and an LF intervention group (L, n = 8). The K group rats were housed under standard conditions, while the rats in M and L groups were individually housed and exposed to CUMS for 44 days simultaneously. Rats in group L were provided with water containing 2% LF for 44 days after the acclimatization period, while the rats in groups K and M were given access to pure water throughout the duration of the experiment. The body weights of the rats were recorded weekly and behavioral tests were performed at week 7. Upon completion of the behavioral tests, the rats were anesthetized with isoflurane and blood was collected from the abdominal aorta. Subsequently, 15-week-old rats were euthanized on day 51 of the experiment using a rat guillotine device.

Chronic unpredictable mild stress

The CUMS experimental design was conducted following the protocol outlined by Xin et al.28, with some minor modifications (Table 1). One randomized stimulus was administered daily for a period of 44 days, with the precaution taken to ensure that the same stimulus was not repeated within a 3-day interval in order to maintain unpredictability.

Table 1 Chronic unpredictable mild stress procedure.
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Behavioural tests

Open field test

The Open Field Test (OFT) is used to assess the independent activity and exploratory behavior of experimental animals in unfamiliar environments29. In this study, OFT as performed on day 7 and day 42, respectively. The rats were gently placed in the center of a black box (50 cm × 50 cm × 50 cm), and the total distance traveled horizontally within 5 min was recorded using Animal Behavioral Analysis software. Throughout the experiment, a quiet environment and stable lighting were maintained. After each test, the chamber was cleaned with 75% ethanol to eliminate any interference from animal odors.

Sucrose preference test

The sucrose preference test (SPT) assesses the sensitivity to reward and the lack of pleasure in experimental animals30. SPT comprises of two phases: training and testing. On the first day, all rats designated for testing were individually housed in cages and provided with both a bottle of sugar water and a bottle of pure water for 24 h to acclimate. During this time, the positions of the water bottles were switched once to eliminate any potential effects of positional preference. On the second day, water was withheld while the food was still available, and on the third day, all rats were simultaneously given a bottle of pure water and a bottle of 1% sucrose solution for a 1-h experiment to measure sugar-water consumption. During this experiment, the positions of the two water bottles were swapped after 30 min, and the ratio of sucrose solution consumption was calculated at the end of the test using the following formula: Sucrose preference = intake of sucrose solution/total liquid consumed × 100%.

Morris water maze

The Morris water maze (MWM) consists of two components: orientation navigation and spatial search, which are utilized to assess the spatial learning and memory abilities of experimental animals31. The pool was divided into four quadrants, and a circular resting platform was placed in the third quadrant, 1 cm below the water surface. Milk was added to the pool until the platform was not visible, and a video camera above the pool synchronously recorded the rat’s movement trajectory. During the first three days of training, rats were placed into the water from four different quadrants (facing the pool wall) to freely search for the platform every day. The time taken by the rats to find the platform within 2 min was recorded. If the rats were unable to locate the platform within 2 min, they were guided to the platform and remained there for 30 s before being removed. On the fourth day, a spatial navigation test was conducted. The rats were placed in the water from the first quadrant, and the time taken to find and climb onto the platform was recorded. On the fifth day, the platform was removed and a spatial exploration experiment was conducted to evaluate the rats’ spatial memory ability. The rats were placed in the first quadrant, and their exploration of the target quadrant (i.e., where the original platform was located) and the number of times they crossed the position of the original platform were recorded over a 120-s period.

Histopathological examination

The jejunal tissue of rats was fixed in 4% paraformaldehyde for 24 h, followed by dehydration and routine paraffin embedding. The tissue slices were 5 μm thick and then baked at 60 ℃ for 2 h. After deparaffinization with xylene, the sections were gradually washed with a graded alcohol solution, stained with hematoxylin–eosin staining solution, dehydrated, and sealed with neutral gum. Histopathological changes were observed under a light microscope and photographed for documentation.

Immunohistochemistry

After dewaxing and rehydrating rat jejunal tissue sections, endogenous peroxidase was blocked using 3% H2O2. High-temperature antigen retrieval was performed in a citric acid solution, followed by blocking with 5% BSA for 30 min. The sections were then incubated with a primary antibody against the tight junction protein ZO-1 (dilution ratio 1:200) and a secondary antibody, and color development was achieved using DAB. Hematoxylin restaining was carried out, followed by returning the sections to blue with tap water, and differentiation with hydrochloric acid in ethanol for 3 s. Tissue sections underwent graded alcohol dehydration before being sealed with neutral tree resin. Finally, the sections were dehydrated in gradient alcohol and sealed with neutral gum.  The sections were viewed under an optical microscope and analyzed using ImageJ18.0 software.

Enzyme-linked immunosorbent assay

Serum concentrations of IL-1β, IL-6, TNF-α, Diamine oxidase (DAO) and LPS were determined by measuring absorbance with an enzyme marker and plotting a standard curve according to the instructions provided in the ELISA kit.

16S rDNA sequencing analysis

The genomic DNA of the samples was extracted using the sodium dodecyl sulfate (SDS) method, and the purity and concentration of the DNA were assessed through agarose gel electrophoresis. PCR amplification of the 16S V4 region was performed with primers 515F (5′-GTTTCGGTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-CAGATCGGACTACHVGGGTWTCTAAT-3′). Library construction was carried out using the TruSeq® DNA PCR-Free Sample Preparation Kit, and up-sequencing was performed by NovaSeq6000. The analysis of differences between groups of Alpha and Beta diversity indices was conducted using R software (v4.2.0) with parametric and non-parametric tests, respectively. Tukey’s test and Kruskal–Wallis’ test were chosen if there were more than two groups. LEfSe analyses were performed using the LEfSe (v1.1.2) software, with the LDA Score filter set to 4 by default. Predictive analyses of the Tax4 Fun function were carried out using the KEGG database32,33.

Untargeted metabolomics analysis

hippocampal sample preparation and extraction

The 20 mg hippocampal sample was homogenized using a grinder (30 HZ) for 20 s. Subsequently, a 400 μL solution (Methanol: Water = 7:3, V/V) containing internal standard was added to the ground sample and shaken at 1500 rpm for 5 min. After incubation on ice for 15 min, the sample was centrifuged at 12,000 rpm for 10 min (4 °C). A 300 μL of supernatant was collected and placed at − 20 °C for 30 min. The sample was then centrifuged at 12,000 rpm for 3 min (4 °C). Finally, 200 μL aliquots of supernatant were transferred for liquid chromatography-mass spectrometry (LC–MS) analysis.

Acquisition conditions for chromatography and mass spectrometry

The T3 chromatographic conditions were as follows: elution on a Waters ACQUITY Premier HSS T3 column (1.8 µm, 2.1 mm * 100 mm). Solvent A is a 0.1% aqueous formic acid solution. 0.1% formic acid dissolved in acetonitrile as solvent B. The column temperature was set at 40 ℃, with a flow rate of 0.4 mL/min and an injection volume of 4 μL (Gradient elution: 5% to 20% over 2 min, followed by an increase to 60% over 3 min, to 99% over 1 min and held for 1.5 min, then back to 5% mobile phase B over 0.1 min and held for 2.4 min). The data acquisition was conducted using the information-dependent acquisition (IDA) mode with Analyst TF 1.7.1 Software (Sciex, Concord, ON, Canada). The source parameters were set as follows: ion source gas 1 (GAS1), 50 psi; ion source gas 2 (GAS2), 50 psi; curtain gas (CUR), 25 psi; temperature (TEM), 550 °C; declustering potential (DP), 60 V, or − 60 V in positive or negative modes, respectively; and ion spray voltage floating (ISVF), 5000 V or − 4000 V in positive or negative modes, respectively. The TOF MS scan parameters were set as follows: mass range, 50–1000 Da; accumulation time, 200 ms; and dynamic background subtract, on. The product ion scan parameters were set as follows: mass range, 25–1000 Da; accumulation time, 40 ms; collision energy, 30 or − 30 V in positive or negative modes, respectively; collision energy spread, 15; resolution, UNIT; charge state, 1 to 1; intensity, 100 cps; exclude isotopes within 4 Da; mass tolerance, 50 ppm; maximum number of candidate ions to monitor per cycle, 18.

Data analysis

The original data file obtained from LC–MS was converted to mzXML format using ProteoWizard software. Peak extraction, alignment, and retention time correction were performed using the XCMS program. The “SVR” method was utilized for peak area correction. Peaks with a detection rate lower than 50% in each sample group were excluded. Subsequently, metabolic identification information was obtained by searching the laboratory’s self-built database, integrated public database, AI database, and metDNA. The integrated public libraries include Metlin (http://metlin.scripps.edu/index.php), HMDB (https://hmdb.ca/), KEGG (https://www.kegg.jp/), Mona (https://mona.fiehnlab.ucdavis.edu/), and MassBank (http://www.massbank.jp/). Unsupervised principal component analysis (PCA) and OPLS-DA were utilized to demonstrate overall metabolic differences and variations between groups. Furthermore, metabolite set enrichment analysis was conducted based on the differentially expressed metabolites using MetaboAnalyst 6.0 (http://www.metaboanalyst.ca).

Sample collection

After administering isoflurane inhalation anesthesia to rats, blood was collected from the abdominal aorta and centrifuged at 4 ℃, 3000 r/min for 15 min. The supernatant was then aspirated and stored at – 80 ℃. The whole brain was craniotomised on ice and the hippocampal tissue was carefully separated, then frozen in liquid nitrogen and stored at − 80 ℃. Colonic feces and jejunum tissues were collected into sterile freezing tubes, frozen in liquid nitrogen, and stored at − 80 ℃. Rat jejunum tissues were taken, rinsed in saline, and placed into a 4% paraformaldehyde tissue fixative.

Statistical analyses

Statistical analysis was conducted using SPSS 25.0 software and GraphPad Prism 8.0 software for data visualization. For data that follows a normal distribution and demonstrates homogeneity of variance, one-way analysis of variance (ANOVA) was utilized to compare multiple groups. Post hoc pairwise comparisons between multiple groups were performed using the LSD test. Data is presented as the mean ± standard deviation, with statistical significance defined as p < 0.05.

Results

Effects of LF on body weight and behaviors in depressed rats

The body mass of rats in group M was significantly lower than that of group K (P < 0.01). There was a tendency for an increase in the body mass of rats after the administration of 2% LF, but the difference was not statistically significant (Fig. 1a). The OFT results indicated no significant difference in the total horizontal distance traveled among the three groups of rats before modelling. Nevertheless, after the combined intervention of CUMS and social isolation, it was observed that the total horizontal distance traveled by rats in group M was significantly lower than that of rats in group K. Additionally, compared to the group M, 2% LF significantly prevented a decline in the total horizontal exercise distance traveled by rats (P < 0.01, Fig. 1b). The activity trajectories of the three groups of rats in the OFT indicated that the 2% LF intervention had the potential to increase voluntary activity in depressed rats (Fig. 1c). The SPT outcomes indicated that the intervention of CUMS combined with individually housed resulted in a reduction in sucrose preference in rats compared to group K. However, the LF intervention significantly prevented the decline in sucrose preference in rats compared to group M (P < 0.05, Fig. 1d). These findings suggest that LF may enhance sensitivity to reward in depressed rats. The trajectory diagrams of the localization navigation trajectories of the three groups of rats indicated that the intervention of 2% LF increased autonomous activity in depressed rats (Fig. 1e). The latency for avoidance behavior was significantly increased in group M compared to group K. After the 2% LF intervention, there was a significant reduction in the avoidance latency of rats (P < 0.05, Fig. 1f). The results of the spatial exploration experiments indicated a significant decrease in the number of platform crossings and dwell time in the target quadrant in group M compared to group K. However, the 2% LF intervention significantly prevented the decrease in both the number of platform penetrations and the residence time of the target quadrant in rats compared to the M group (P < 0.05, Fig. 1g-h). Furthermore, the results of the MWM demonstrated that LF significantly improved learning and memory abilities in depressed rats. Taken together, these behavioral results suggest that 2% LF can alleviate depressive-like behavior in depressed rats.

Fig. 1
figure 1

Effects of LF on body weight and behaviors in depressed rats. (a) Weekly changes in body mass of rats. (b) Total horizontal distance traveled in the OFT. (c) Trajectory map of three groups of rats on day 7 and day 42 of the OFT. (d) Sucrose preference in three groups of rats. (e) Trajectory map of localization voyage in three groups of rats. (f) Latency of avoidance behavior in the MWM. (g) Frequency of platform crossings in the MWM. (h) Duration of stay in the target quadrant. Note: Compared to group K, *p < 0.05, **p < 0.01; and compared to group M, #p < 0.05, ##p < 0.01. K control group; M model group; L LF treatment group.

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Effects of LF on jejunal tissue and intestinal mucosal barrier in depressed rats

The HE results of jejunal tissue from three groups of rats showed (Fig. 2a) that in group K, the mucosal, submucosal, muscular, and plasma layers were more structurally intact, and the intestinal villi were neatly arranged. Intestinal villi were reduced and disorganized, some of them are broken and detached in jejunal tissue of group M. The histological structure of the jejunal mucosa in group L was more intact compared to group M. The length of the intestinal villi was increased and better aligned, and the pathological changes were alleviated. Tight junctions are important structures for maintaining the mechanical barrier and permeability of the intestinal mucosal epithelium, of which ZO-1 is an important component34. ZO-1 expression was significantly decreased in group M compared to group K. After LF intervention, intestinal ZO-1 protein expression was increased in group L compared to group M, and intestinal mucosal permeability was decreased (Fig. 2b-c, P < 0.05).

Fig. 2
figure 2

Effects of LF on jejunal tissue and intestinal mucosal barrier in depressed rats (a) jejunal tissues stained with HE (b) ZO-1 expression in jejunal tissue from three groups of rats (c) The relative expression of ZO-1 is indicated by the mean optical density (MOD). Note: Compared to group K, *p < 0.05, **p < 0.01; and compared to group M, #p < 0.05, ##p < 0.01. K control group; M model group; L LF treatment group.

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Effects of LF on serum levels of pro-infammatory cytokines, LPS and DAO in depressed rats

As shown in the figure (Fig. 3a-c), the levels of serum TNF-α, IL-1β, and IL-6 were significantly higher in group M compared to group K. After LF intervention, TNF-α, IL-1β, and IL-6 were significantly decreased in group L compared to group M. Diamine oxidase (DAO) is an intracellular enzyme found in intestinal epithelial cells, while LPS is a bacterial metabolite produced by the intestinal flora35. The serum levels of DAO and LPS are crucial indicators for evaluating and assessing the extent of impairment of intestinal barrier function and permeability36. Serum levels of DAO and LPS were significantly higher in group M compared to group K. Following a 2% LF intervention, serum levels of DAO and LPS were significantly reduced in group L compared to group M (Fig. 3d-e).

Fig. 3
figure 3

Effects of LF on levels of pro-inflammatory cytokines, DAO and LPS in depressed rats. (a) Level of TNF-α. (b) Level of IL-1β. (c) Level of IL-6. (d) Level of DAO. (e) Level of LPS. Note: Compared to group K, *p < 0.05, **p < 0.01; and compared to group M, #p < 0.05, ##p < 0.01. K control group; M model group; L LF treatment group.

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Effects of LF on intestinal flora in depressed rats

Effects of LF on gut microbial diversity in depressed rats

Alpha diversity is utilized to represent the abundance and variety of species within the group. Chao1 and ace indices reflect the richness of the communities in the samples, shannon index reflects the diversity of the communities. The box plots of chao1, ace, and shannon indices visualized (Fig. 4a-c) that the combined induction of CUMS and solitary rearing significantly increased the intestinal flora diversity of rats compared to the normal group, and the LF intervention moderated the changes in the intestinal flora of the rats, bringing the intestinal flora diversity of the rats in group L closer to that of the normal group (P < 0.05).

Fig. 4
figure 4

Effects of LF on gut microbial diversity in depressed rats. (a) Indices of chao 1. (b) Indices of ace. (c) Indices of shannon. (d) Beta diversity of PCoA. There was a noticeable difference in the composition of the flora structure between the K and M groups. Additionally, it was observed that the structure of intestinal flora in the L group rats closely resembled that of K group. Note: *p < 0.05, **p < 0.01. K control group; M model group; L LF treatment group.

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β-diversity is an indicator for assessing differences between groups of microbial communities. In this study, unweighted UniFrac distance was used for principal coordinate analysis PCoA. The PCoA analysis (Fig. 4d) revealed a significant difference in the composition of bacterial flora structure between group K and group M, indicating that the intestinal flora structure of rats with depression was disrupted. Furthermore, the intestinal flora structure of group L deviated from the model group and closely resembled that of the normal group, suggesting that LF could restore the intestinal flora structure of depressed rats to a normal state.

Intestinal microflora structure among three groups

There is mounting evidence suggesting that the gut microbiome plays a significant role in the development of depression37. A cumulative plot of relative species abundance at the phylum level shows (Fig. 5a) that the Firmicutes/Bacteroidetes ratio was elevated in depressed rats after LF intervention. After conducting the independence test, normality test, and variance chi-square test, a t-test was performed on two independent samples. Species with significant differences between groups were identified at each taxonomic level (p < 0.05). At the phylum level, there was a significant increase in Bacteroidta and a significant decrease in Firmicutes in group M compared to group K (Fig. 5b). At the family level, Rikenellaceae was significantly lower after the LF intervention compared to the M group (Fig. 5c). At the genus level, Alistipes significantly decreased and Limosilactobacillus significantly increased after LF intervention compared to group M (Fig. 5d).

Fig. 5
figure 5

Effects of LF on gut microbial composition of depressed rats. (a) Composition at the phylum level. The ratio of Firmicutes and Bacteroidetes (F/B) increased in L group relative to M group. (b) t-test analysis at the phylum level. (c) t-test analysis at the family level (d) t-test analysis at the genus level. (e) Histogram of LDA analysis. When species with LDA Score > 4 are statistically different, the length of the histogram (LDA Score) represents the impact size of the different species. (f) The distribution difference of flora was analyzed using LEfSe. Note: Compared to group K, *p < 0.05, **p < 0.01; and compared to group M, #p < 0.05, ##p < 0.01. K control group; M model group; L LF treatment group.

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Linear discriminant analysis effect size (LEfSe) was employed to identify and resolve statistically significant differences in marker flora (P < 0.01, LEfSe score > 4.0) between groups. As depicted in the figure (Fig. 5e), the LefSe analysis revealed 12 marker flora. In the K group, the dominant floras were Lactobacillaceae, Lactobacillales, and Bacilli. The M group exhibited marker flora including Ruminococcus, Colidextribacter, Clostridia, Oscillospiraceae, and Prevotellaceae. As for the L group, the dominant flora comprised Lactobacillus-reuteri, Limosilactobacillus, Intestinmonas, and Ruminococcaceae. Evolutionary branching trees within a clade illustrate the classification levels from phylum to species. The red nodes represent the significant microbial groups in group K, while the blue nodes signify the importance of microbial groups in group M. The green nodes represent the significant microbial groups in group L. Additionally, yellow nodes indicate species that do not show significant differences (Fig. 5f). The evolutionary relationships of marker species are as follows: Firmicutes—Bacilli—Lactobacillales—Lactobacillaceae—Limosilactobacillus—Lactobacillus_reuteri; Bacteroidota—Bacteroidia—Bacteroidales—Rikenellaceae—Alistipes.

Functional properties predicted by Tax4Fun2 of LF on intestinal microflora function in depressed rats

The Tax4Fun2 software was utilized to compare the species composition information, in order to deduce the functional gene composition within groups. Annotations categorized by tertiary KEGG pathways (Fig. 6a). Significant differences in numerous functional pathways between group M and group L were uncovered through t-test analysis (Fig. 6b). Pyrimidine metabolism, lipid metabolism, and alanine, aspartate, and glutamate metabolism exhibited a significant increase in the LF intervention group compared to the M group (p < 0.05).

Fig. 6
figure 6

The KEGG prediction of the intestinal microbiota. (a) Annotations of functional gene. (b) Analysis of expression levels using t-test. Note: The difference was deemed statistically significant when P < 0.05. K control group; M model group; L LF intervention group.

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Effects of LF on hippocampal metabolites in depressed rats

The PCA of the metabolite profiles from hippocampal tissues of the three groups of rats is illustrated in Fig. 7a, demonstrating a noticeable separation trend among the three groups. This indicates significant differences in metabolic profiles. The validation results of the OPLS-DA model indicate that, in the comparison between group K and group M, R2Y = 0.987 and Q2 = 0.807, demonstrating successful replication of the depression model (Fig. 7b). In the comparison between group M and group L, R2Y = 0.991 and Q2= 0.764, suggesting a clear effect of the LF intervention and high data reliability (Fig. 7c). As illustrated in the OPLS-DA plot (Fig. 7d), it is evident that groups K and M are distinctly separable, indicating significant changes in metabolites between group M and group K. Following the LF intervention, there was a noticeable divergence in hippocampal metabolite expression levels in group L compared to those of group M, displaying a shift back towards the pattern observed in group K. Differential metabolites were identified based on VIP (VIP > 1), P-value (P-value < 0.05, Student’s t-test), and Fold Change (FC > 1.2) criteria. VIP values were extracted from OPLS-DA results. In comparison to group K, group M demonstrated a significant up-regulation of 516 metabolites and a significant down-regulation of 134 metabolites (Fig. 7e). Conversely, when compared to group M, group L exhibited a significant up-regulation of 370 metabolites and a significant down-regulation of 180 metabolites (Fig. 7f). A total of 36 differential metabolites, such as 5-hydroxy-L-tryptophan and 5-hydroxy-indole-3-acetic acid, were identified in the comparison among the three groups (Fig. 8). Following a 2% LF intervention, these differential metabolites were significantly regulated back towards the normal group. The KEGG enrichment analysis of the aforementioned differential metabolites suggests that LF may exert its antidepressant effects through various metabolic pathways, including glyceropholipid metabolism and tryptophan metabolism (Fig. 9).

Fig. 7
figure 7

Effects of LF on the metabolism of the hippocampus in depressed rats (a) three-dimensional diagram of PCA. (b) Validation of the OPLS-DA model between groups K and M, where a Q2 value > 0.5 is considered as an effective model. (c) Validation of the OPLS-DA model between groups M and L, where R2 and Q2 values closer to 1 indicate a more stable and reliable predictive effect of the model. (d) Scores OPLA-DA plot. (e) Differential metabolite volcano plot comparing groups M and K. (f) Differential metabolite volcano plot comparing groups L and M.

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Fig. 8
figure 8

 Violin diagram of differential metabolites among the three groups. Note: The top 10 differential metabolites are presented in order of p-value. * p < 0.05 and ** p < 0.01 indicate significance compared to group K; # p < 0.05 and ## p < 0.01 indicate significance compared to group M. K control group; M model group; L LF intervention group.

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Fig. 9
figure 9

KEGG enrichment analysis of differential metabolites. KEGG enrichment analysis of the aforementioned differential metabolites revealed that LF may exert its antidepressant effects through various metabolic pathways, such as glyceropholipid metabolism and tryptophan metabolism.

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Behavioural, inflammatory factor, differential flora and differential metabolite correlation analysis

Two-by-two correlation analyses were conducted using Spearman’s correlation analysis to investigate the relative levels of behavioral and inflammatory factors, differential intestinal flora, and differential metabolites in rats. As depicted in Fig. 10, the total distance traveled during the open field test level of exercise showed a significant positive correlation with the tryptophan metabolizing bacteria Lactobacillus and Lactobacillus-johnsonii, as well as with the hippocampal metabolites 5-Hydroxy-L-tryptophan and 5-Hydroxy-Indole-3-Acetic Acid. Furthermore, there was a notable positive correlation with the pro-inflammatory bacteria Bacteroidota, Rikenellaceae, Tannerellaceae and Alistipes. Conversely, serum LPS, IL-6, and IL-1β exhibited a significant negative correlation. The findings suggest that the therapeutic effect of LF as an antidepressant is linked to a variety of gut flora and hippocampal metabolites. Furthermore, it was observed that pro-inflammatory, tryptophan-metabolizing bacteria and hippocampal metabolites in the gut co-regulated depressive-like behavior in depressed rats.

Fig. 10
figure 10

Correlation analysis of behavior, inflammatory factors, differential flora, and differential metabolites. Note: The color red indicates a positive correlation, while blue indicates a negative correlation. * p < 0.05, and ** p < 0.01.

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Discussions

Chronic stress plays a critical role in the pathogenesis of depression and is a significant contributing factor to its development38. In the current study, we established a rat model of depression by utilizing a combination of CUMS and solitary rearing. The clinical manifestations of depression often include anhedonia, psychomotor retardation, and cognitive impairment39. In animal models of depression, the sucrose preference test is commonly utilized to evaluate the animals’ hedonic response, the open field test is employed to assess their locomotor activity in depressed animals, and the Morris water maze is utilized to measure their spatial memory40. In this study, rats in group M demonstrated a reduced preference for sucrose, decreased voluntary activity, and spent less time in the target quadrant compared to group K. These findings suggest the successful establishment of a rat model for depression. Furthermore, treatment with LF significantly improved the previously mentioned abnormal behavioral indicators. The results of behavioral tests consistently demonstrated that LF had a moderating and alleviating effect on depression-like behaviors in depressed rats, which is consistent with the findings of previous researchers18,19. However, there have been no studies investigating the moderating effect of LF on metabolic disorders in depressed rats, particularly its impact on intestinal flora disorders and the subsequent immune-inflammatory response. The innovation of this study lies in its pioneering use of 16S rDNA gene sequencing and LC–MS untargeted metabolomics to investigate the impact of LF on the intestinal flora and hippocampal tissue metabolites in depressed rats. This research will serve as a valuable reference for further exploration into the mechanism by which LF alleviates depression.

The dysbiosis of gut flora is believed to be a contributing factor to the development of depression through inflammation41. The results of this study showed that the structure of the intestinal flora of group M rats was altered under the induction of CUMS and solitary rearing, and the LF intervention could regress the intestinal diversity and abundance of depressed rats back toward normal levels, which is consistent with the phenomenon observed in clinical studies42. At the phylum level, LF intervention resulted in an increase in the Firmicutes/Bacteroidetes (F/B) ratio. Recent studies have indicated a reduction in the abundance of Firmicutes in the gut flora of patients with major depression43, and Bacteroidetes, a gram-negative bacterium, which releases LPS that is absorbed into the bloodstream during increased intestinal permeability and induces peripheral and central pro-inflammatory cytokine release44. It is evident that elevated F/B inhibits inflammation. Additionally, our study found that LF intervention led to a decrease in the abundance of Alistipes and Rikenellaceae. Alistipes not only have significant pro-inflammatory effects but also degrade tryptophan in the gut, affecting the mental health of the host and being a potential biomarker for the diagnosis of depression45. Several studies have demonstrated a significant negative correlation between Rikenellaceae and intestinal tight junction proteins, as well as a positive correlation with inflammatory cytokines and the severity of depression46,47.

Furthermore, the experimental findings of this study demonstrated a significant increase in the expression of ZO-1 in depressed rats following LF intervention. This was accompanied by a reduction in intestinal permeability and a decrease in the expression of inflammatory factors and LPS in serum. Altered intestinal permeability plays a crucial role in the microbe-gut-brain axis and is closely associated with depression. Depressed patients often demonstrate heightened intestinal permeability, increased levels of plasma LPS, disruption of the integrity of the blood–brain barrier, and the entry of significant amounts of inflammatory factors into brain tissue48,49. In conclusion, LF has the potential to ameliorate depressive-like behaviors in rats by modulating the composition of intestinal microorganisms, reducing intestinal permeability, and attenuating peripheral inflammation.

Lipids make up more than 50% of the brain’s dry weight, and lipid metabolism plays a crucial role in brain function50. Research has indicated that both peripheral and central lipid metabolism are disrupted in individuals with depression51. The gut microbiota, which serves as a key regulator of lipid metabolism, can have a significant impact on both peripheral and central lipid metabolism in the host52. It has been reported that disruptions in the intestinal flora can contribute to the development of depressive-like behaviors by inducing disturbances in glycerophospholipid metabolism, both peripherally and centrally, in depressed animals53. The Tax4Fun2 prediction in this study suggests that LF intervention resulted in an upregulation of lipid metabolism genes expression in the intestinal flora of depressed rats. Previous studies have shown that LF is involved in the regulation of adipocyte growth and differentiation, as well as playing a role in lipid metabolism54. Furthermore, clinical studies have demonstrated that individuals with major depressive disorder (MDD) have significantly lower total cholesterol (TC) and low-density lipoprotein (LDL) concentrations compared to healthy controls55, and that the decrease in TC is correlated with the severity of MDD56.

In this study LF intervention significantly increased the abundance of Lactobacillus-reuter, a beneficial bacterium with tryptophan metabolizing capacity. Furthermore, untargeted metabolome results revealed that LF intervention significantly elevated the content of 5-Hydroxytryptophan and 5-Hydroxyindoleacetic acid in the hippocampal tissues of depressed rats. Additionally, KEGG enrichment analysis of differential metabolites indicated that LF’s alleviation of depression-like behavior was closely associated with tryptophan metabolism. Tryptophan is an essential amino acid that cannot be synthesized endogenously in humans or animals and must be obtained through dietary intake. The hydrolysis of LF by pepsin results in the production of a class of small antimicrobial peptides that are rich in tryptophan57. The metabolism of tryptophan has been found to play a significant role in the interaction between gut flora and the brain in depressed animals58. The primary metabolite of tryptophan in the intestinal flora, indoles, not only induces the expression of tight junction proteins and anti-inflammatory mediators in intestinal epithelial cells59, but also crosses the blood–brain barrier and participates in regulating the central nervous system to exert anti-inflammatory effects60. Furthermore, the findings of Spearman’s correlation analysis provide additional support for the idea that LF reduces depression-like behaviors through the regulation of microbial-gut-brain interactions.

Several limitations of this study are as follows: Firstly, this study investigated the modulatory effect of LF on metabolic disorders in depressed rats using a combined 16S rDNA gene sequence analysis and metabolomics. However, the exact mechanism of the antidepressant effect has yet to be confirmed, and further experiments to verify this mechanism will be conducted at a later stage.In addition, stress resilience is an important variable in animal models. The results of this experiment may include animals that are resilient to stress.

Conclusions

In conclusion, the gut microbiota is a critical target for LF in providing additional therapeutic benefits for depression. Moreover, the microbiota-gut-brain axis serves as a significant mechanism through which LF can effectively prevent and treat depression. LF can regulate the dysbiosis of intestinal flora in depressed rats. It is capable of reducing the abundance of pro-inflammatory bacteria, enriching tryptophan-metabolizing bacteria, promoting the metabolism of tryptophan to indoles and mass-lipid metabolism, decreasing intestinal permeability, and limiting the entry of harmful substances such as LPS into the bloodstream. Additionally, it can further modulate the metabolic homeostasis of hippocampal tissues through the microbiota-gut-brain axis to reduce inflammation in both peripheral and central regions. Ultimately, this alleviates depressive-like behaviors in depressed rats. This study lays the foundation for understanding the mechanism of LF in regulating depression and is expected to facilitate the development of functional foods containing LF.