Anti-Alzheimers molecular mechanism of icariin: Insights from gut microbiota and metabolome combined network pharmacology

doi:10.21203/rs.3.rs-2487902/v1

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Research Article

Anti-Alzheimers molecular mechanism of icariin: Insights from gut microbiota and metabolome combined network pharmacology

https://doi.org/10.21203/rs.3.rs-2487902/v1

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Journal Publication

published 24 Apr, 2023

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Background

Icariin (ICA), an active ingredient extracted from a natural plant called Epimedii, has shown a broad application prospect in the treatment of Alzheimer's disease (AD). Nevertheless, its potential therapeutic mechanism remains largely unknown.The present study aims to investigate the therapeutic effect and the underlying mechanism of ICA on AD by an integrated analysis of gut microbiota and metabolomics.

Methods

The cognitive impairment of mice was measured using the Morris Water Maze. Hematoxylin and eosin staining was used to identify the AD-induced pathologic change. 16S rRNA sequencing and multi-metabolomics were performed to analyze the alterations in microbiota and serum/fecal metabolism.

Results

Our results demonstrated that ICA intervention could markedly improve cognitive dysfunction in APP/PS1 mice and typical AD pathologies in the hippocampus of APP/PS1 mice. Gut microbiota analysis showed that ICA administration could reverse the imbalance of gut microbes in APP/PS1 mice by elevating the proportion of Akkermansia and reducing the proportion of Alistipe. Metabolomic analysis revealed AD-induced metabolic disorder was improved by ICA via glycerophospholipid metabolism and sphingolipid metabolism. Correlation analysis suggested that these were closely related to the abundance of Alistipe and Akkermansia.

Conclusion

These findings indicated that ICA may serve as a promising therapeutic approach for AD, and that the ICA-mediated protective effects were associated with the amelioration of microbiota disturbance and metabolic disorder.

Icariin

Alzheimer's disease

APP/PS1 mice

Akkermansia

Alistipe

sphingolipid metabolism

Network pharmacology

Alzheimer's disease (AD) is a progressive neurological condition that cannot be reversed. Clinically, AD is predominantly characterized by cognitive and memory deterioration. These symptoms account for 70% of all cases of AD, involving more than 35 million people around the world. Furthermore, owing to its direct and indirect costs, AD is becoming an increasingly important issue for public health systems given the aging population of the planet [1]. Despite the considerable progress accomplished in the past decade, there are no effective treatments available for AD.

Icariin (ICA) is an active component of the genus Epimedii with various biological actions [2]. Numerous studies have shown that ICA and its metabolites can improve neuronal survival and function by reducing the production of extracellular Aβ [3] and intracellular neurofibrillary tangles (NFTs). Moreover, ICA can inhibit the activity of phosphodiesterase-5 to restore neurovascular function which is beneficial to AD [4]. In terms of neuroinflammation, ICA can inhibit the expression of proinflammatory factors such as tumor necrosis factor (TNF)-α [5] and interleukin-1β [6]. ICA could also prevent H2O2-induced neurotoxicity by decreasing reactive oxygen species production through up-regulating silent mating type information regulation 2 homolog- 1 (SIRT1) [7]. Furthermore, substantial studies indicate that ICA can cause alterations in the abundance, proportion and function of gut microbiota in AD and age-related maladies[8, 9]. However, the underlying mechanism through which ICA impacts on AD remains poorly understood, impeding progress in the development of therapeutic functions and the clinical usage of ICA. Thus, further investigations are warranted to explore how ICA might benefit humanity.

The application of multi-omics and multi-dimensional analysis was intended to promote better understanding of disease development and drug therapeutic mechanisms. 16S ribosomal RNA (16S rRNA) gene sequencing is regarded as an effective means of bacterial analysis. The metabolomics technology can track dynamic changes in metabolites with molecular weights under 1000 using high-throughput instruments [10], and can evaluate the metabolic effects of food [11]. In recent years, the emergence of network pharmacology has made it viable to systematically explore substances [12]. Combined with intestinal flora analysis and metabolomics, we can now conduct in-depth research on ICA.

In the present study, the Morris Water Maze (MWM) test and Hematoxylin-eosin (HE) staining were employed to assess the impact of ICA on cognitive performance and histopathological changes in APP/PS1 mice. Moreover, 16s rRNA analysis was applied to examine gut changes in the mice. The regulatory effect of ICA on metabolic profiles in serum and feces was displayed by liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics. Furthermore, network pharmacology was integrated to highlight the current scientific understanding of ICA in the treatment of AD. This research intends to provide new light on the intricacy of AD biology and discover regulatory networks for therapeutic targeting of ICA in AD.

2.1 Animal modeling, grouping, and intervention

This study was carried out following the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institute of Health. All procedures that involved animals were approved by the Animal Ethics Committee of the Second Xiangya Hospital, Central South University (2020038). All procedures were conducted under pentobarbital sodium anesthesia, and discomfort was kept to a minimum. 250–300 g, 6-month-old C57BL6/J and APP/PS1 mice were obtained from Spife (Beijing) Biotechnology Co., Ltd. Every mouse was kept in a regulated habitat (50 ± 10% relative humidity, 12/12 h light/dark cycle, 22 ± 2°C) with free access to standard food and water. APP/PS1 mice were randomly allocated into two groups: model group (n = 10) and ICA group (n = 12), whereas C57BL6/J mice were assigned to the control group (n = 10). The ICA group was administered ICA at a dosage of 100 g/(kg*d) for 100 days, whilst the other two groups received the same quantity of sterile saline solution

2.2. Morris Water-Maze (MWM) test

The Morris Water Maze (MWM) test, which consists of a spatial acquisition test and a spatial probe test, was used to assess spatial learning and memory abilities [12]. The spatial acquisition test was carried out between days 94 and 98 to assess the animals' spatial learning abilities by a 5-day memory acquisition experiment. On day 99, a spatial probe experiment was carried out to test the spatial memory retention ability of the mice.

2.3. Hematoxylin and eosin (HE) staining

Following completion of MWM tasks, the mice were anesthetized and transcardially perfused with 300 mL of stroke-physiological saline solution to eliminate their blood. As in our previous work, brain tissue was collected, treated with 4% paraformaldehyde solution, embedded in paraffin, and cut into 4–5 µm thick slices [13]. Then, the sections were stained with hematoxylin and eosin solution and sealed with neutral gum. Finally, pathological changes in the brain tissue were observed and photographed with an optical microscope.

2.4. 16S rRNA Gene Sequencing analysis

2.4.1 Collection of intestinal contents

At 100 days of age, at least five fecal pellets were taken from the rectum of each mouse, put in a sterile conical tube, and frozen at -80°C until further processing could take place.

2.4.2. Gut data acquisition and processing

The 16S rRNA sequencing analysis methodology was carried out as stated in our prior studies [14]. Briefly, Polymerase Chain Reaction (PCR) amplification, pooling of purified amplicons, and paired-end sequencing were conducted. The raw data was then evaluated. The entire sequencing analysis methodologies may be found in the Supplementary Material and Methods, and the data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under BioProject ID: PRJNA918352.

2.5.LC-MS based metabolomics

2.5.1 Sample collection and preparation

At the age of 100 days, feces were collected and at least five fecal pellets were retrieved from the rectum of each mouse and placed in a sterile conical tube. In addition, after the MWM test, the mice were killed under anesthesia, and blood was obtained through the abdominal aorta. All materials obtained were immediately frozen at -80°C for further examination. When preparing for omic testing, the samples are taken out and recovered to prepare qualified samples. More detailed analysis procedures are available in the Supplementary Material and Methods.

2.5.2 LC-MS data acquisition and processing

The UltiMate 3000 Ultra High-performance Liquid Chromatograph (Thermo Fisher, USA) and Thermo Q-Exactive Orbitrap Mass Spectrometer were used for UPLC-MS/MS analysis (Thermo Fisher). Chromatographic separations were performed on Waters ACQUITY UPLC HSS T3(100 mm×2.1 mm, 1.8 um). For further analysis, all raw data were put into Progenesis QI (Waters, Milford, MA, USA) and SIMCA-P14.0 (Umetrics AB, Umea, Vasterbotten, Sweden), software [15]. The partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) supervised pattern recognition methods were applied to identify the overall metabolic differences among the groups. The variable importance in the projection (VIP) was used to identify characteristic metabolites in the three groups. On the basis of VIPs > 1.0 and P < 0.05, distinct metabolites were evaluated. More thorough analytic processes are accessible in the Supplemental Material and Methods.

2.6. Gut microbiota-metabolite correlation analysis

The differential metabolites engaged in important metabolic pathways between the ICA group ICA and the model group were studied via correlation analysis, with the top 45 species identified in the abundance of intestinal flora. Spearman correlation analysis was then performed and heatmaps were created

2.7. Network pharmacology

Firstly, the drug targets were anticipated by PubChem (https://pubchem.ncbi.nlm.nih.gov/)[16], Pharm Mapper (http://www.lilab-ecust.cn/pharmmapper/) and Swiss Target Prediction (http://www.swisstargetprediction.ch/). All the retrieved targets were transformed into their official symbols using the UniProt Knowledgebase search function (http://www.uniprot.org/) in the Protein Database (UniProt). Gene Cards (https://www.genecards.org/) and OMIM (https://omim.org/)[17] were utilized to gather the AD-related genes. Then, promising therapeutic targets were submitted to STRING (https://string-db.org/) for protein-protein interaction (PPI) discovery, and Cytoscape 3.8.0 was used to form a PPI network. To further investigate the biological function and probable processes of the identified targets, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were undertaken. Finally, the hub genes involved in significant pathways were selected for subsequent molecular docking analysis by PyMOL 2.4.0 and Autodock software. More detail on analytic processes is accessible in the Supplemental Material and Methods.

2.8. Statistical analysis

SPSS 20.0 software was used to analyze the data presented as the mean ± standard deviation (SD) (IBM, Armonk, NY, USA). The escape latency was analyzed by separate repeated measures of two-way analysis of variance (ANOVA). Other data were evaluated by one-way ANOVA. P < 0.05 was deemed statistically significant.

3.1. ICA alleviated cognitive impairment and histopathological changes in APP/PS1 mice

To examine the effects of ICA on cognitive deficits in APP/PS1 mice, MWM tests were performed to detect and evaluate the spatial learning and memory capacities of mice. With daily practice, the escape latency data of navigation trials were decreased drastically. On the fifth day, the spatial probe test was administered, and the time spent in the target quadrant and frequency crossing target quadrant was recorded. The findings demonstrated that the APP/PS1 mice had a regular and discrete swimming trajectory, a diminished number of platform crossings, and a longer escape latency compared to the control group (p < 0.05, Figure 1B, D). The ICA-treated APP/PS1 mice had an intricate and disordered swimming trajectory, a significant rise of platform crossings (p＜0.05, Figure 1C, D), and a significantly shorter escape latency than the vehicle treated APP/PS1 mice (p<0.05, Figure 1B). These findings showed that ICA therapy might improve the spatial learning and memory deficits observed in APP/PS1 mice.

Additionally, we conducted HE staining to demonstrate the impact of ICA on the histopathological changes of APP/PS1 mice. Critical for learning and memory, the hippocampus is particularly susceptible to injury in the early stages of AD [18]. Figure 1E, F shows that the nerve cells in the hippocampus in the control group were neatly organized and spherical, with clear cell membranes and nuclei, and no evident swelling or necrosis. The nerve cells in the model group were exceedingly disorganized and uneven in size and structure, with the quantity drastically reduced and the shape distorted. After ICA-treatment in APP/PS1 mice, the integrity of nerve cells was preserved and there was no evidence of nerve cell necrosis. Taken together, our findings suggest that ICA therapy might alleviate learning and memory deficits as well as histological alterations in APP/PS1 mice.

3.2. ICA altered the composition of gut microbiota in APP/PS1 mice

To determine whether the gut microbiota is associated with the anti-AD benefits of ICA in APP/PS1 mice, we sequenced the 16S rRNA gene amplicon on 32 samples of feces from C57 mice (n=10), vehicle treated APP/PS1 mice (n=10), and ICA-treatment APP/PS1 mice (n=12). As the results show, the Ace index and Chao index revealed that ICA could restore the abundance of intestinal flora in AD mice (Supplement.fig 1C, D), while the Sannon index and Simpson index showed that there was no significant effect on species richness (Supplement.fig 1A, B). Meanwhile, partial least squares discrimination analysis (PLS-DA) demonstrated that the samples were separated in groups, indicating a change in the gut bacterial community among the three groups (Figure 2A). To evaluate the general makeup of gut microbiota across the three groups, we examined the degree of taxonomic similarity between the bacteria. Bacteroidetes (46-54%) and Firmicutes (40-47%) were the leading phyla among the three categories at the phylum level (Figure 2B). Of note, the comparative abundance ratio of Firmicutes/Bacteroidetes in the model group (1.02) was up-regulated compared to the control group (0.74), while the ratio of Firmicutes/Bacteroidetes showed a downward trend after the ICA intervention (0.86). At the genus level, certain bacteria related to lipid metabolism were concerning such as Akkermansia, Parabacteroides and Alistipes. It should be noted that the abundance of Akkermansia has the effect of affecting blood lipid and blood glucose [19], while increased Alistipes is associated with a higher-fat diet [20]. In this study, the abundance of Akkermansia tended to increase after the ICA intervention (p<0.01, Figure 2C), while the abundance of Alistipes was decreased by ICA (p<0.05). Meanwhile the inflammation-associated microbiota Mucispirillum[21] was decreased by ICA (p<0.05). The linear discriminant analysis (LDA) effect size (LEfSe) method was used to identify the key bacterial taxa composition in each group, with all the validated sequences analyzed (Figure 2 E). APP/PS1 mice treated with ICA exhibited an increased abundance of Akkermansia, while vehicle treated mice had an increased abundance of Mucispirillum (Figure 2D). The KEGG Pathway Database 16S rRNA gene sequences were predicted using PICRUSt (Figure 2 F). Intriguingly, we noticed that the enhanced pathways in the ICA group were mostly linked with metabolism, including glutamine, amino acid, and energy metabolism, with certain differences among the groups. Overall, these findings suggested that the microbiome-metabolism-brain axis underlies the anti-AD protective actions of ICA.

3.3. ICA reversed fecal metabolomics disorder in APP/PS1 mice

To further explore the underlying mechanisms accounting for the anti-AD preventive effects of ICA, we conducted fecal metabolomics on C57 mice, vehicle-treated APP/PS1 mice, and ICA-treated APP/PS1 mice. The fecal metabolic profiles of the three groups were significantly separated by PLS-DA (Figure 3A, B) and OPLS-DA (Supplement.fig 2A, B) analysis, which indicated that ICA-treatment could partly reverse AD-induced metabolic disorders. At the individual metabolite levels, there were 380 differential metabolites (126 upregulated/254 downregulated) between the control group and the model group, and 687 different metabolites (265 upregulated/422 downregulated) between the ICA group and the model group via VIP strategy. The classified metabolites revealed that lipids and lipid-like molecules (17.5%) and fatty acyls (17.5%) were listed as the two most prominent metabolites (Supplement.fig 2C). Figure 3C-F outlines the relative abundances of fecal metabolites in the three groups, demonstrating that the abundances of the majority of fecal metabolites in the model group were considerably different from those in the control group and that ICA treatment had a measurable influence on fecal metabolics. Of note, sphingolipids showed significant differences among all groups. We then examined the taxonomic makeup of the sphingolipid metabolites. The model group exhibited higher levels of sphingolipids like Cer-AS d37:5, Cer-NP t35:1, Cer-NP t19:1/16:0 and CerP 32:0, while Cer-NDS d32:0, Cer 36:2, Cer-BS d35:1 and Cer-AP t42:0 were down-regulated after ICA-treatment (Supplement.fig 2D). Furthermore, the results of pathway analysis showed that both AD and ICA contribute to changes in sphingolipid metabolism (p=0.0081761, 2.40E-05) (Figure 3G, H). Through comprehensive analysis, we have demonstrated that ICA is likely to improve the disease by reversing the effects of AD on sphingolipid metabolism in feces.

3.4. ICA reversed serum metabolomics disorder in APP/PS1 mice

The above findings imply that ICA may affect the metabolic profile of the gut microbiota in APP/PS1 mice. To investigate further the possible impact of ICA on APP/PS1 mouse serum metabolites, we conducted UPLC-MS/MS-based untargeted serum metabolomics. PLS-DA (Figure 4 A, B) and OPLS-DA analysis (Supplement.fig 3 A, B) showed that the AD mice had obvious metabolic disorders and ICA could partly restore the disorders. A total of 99 differential metabolites (45 upregulated/54 downregulated) between the control group and the model group and 111 differential metabolites (40 upregulated/61 downregulated) between the ICA group and the model group were identified via VIP strategy. Among these, lipids and lipid-like molecules (22,6%) made up the greatest proportion of differentiated metabolites between the ICA group and the model group (Supplement.fig 3C). The relative abundances of serum metabolites, particularly glycerophospholipids, revealed that ICA administration had a dramatic influence on the metabolic profiles of serum (Figure 4C-F). Thus, we explored the taxonomic distribution of glycerophospholipid metabolites in more detail. The levels of PE 40:9e, PE (18:1/22:6) and PI (16:0/20:4(5Z,8Z,11Z,14Z)) were significantly higher in the model group than in the control group, while PC (3:0/3:0), lysoPC 18:4, PS (16:0/18:0) were down-regulated after ICA-treatment. However, PE (20:5(5Z,8Z,11Z,14Z,17Z)/P-18:1(11Z)) and PC (18:2(15E,17E)/18:2(15E,17E)) [U] were also up-regulated in the ICA group compared to the model group (Supplement.fig 3D). As expected, metabolic pathway analysis indicated the ICA may have regulated glycerophospholipid metabolism (p=0.0046882 in the control group vs. the model group; p=0.036736 in the ICA group vs. the model group) and sphingolipid metabolism (p=0.017438 in the control group vs. the model group; p=0.013173 in the ICA group vs. the model group) in the serum of APP/PS1 mice (Figure 4G, H). Collectively, these findings suggest that ICA modulates the metabolism of the gut microbiota and the consequent synthesis of metabolites, particularly sphingolipids and glycerophospholipids, which impacts the host through circulation.

3.4. Interaction between gut microbiota and metabolites

To examine further the relationship between the abundances of metabolites and gut microbiota for ICA-protective effects on AD, Spearman analysis was performed between 45 genera and 26 fecal metabolites, as well as between 45 genera and 14 serum metabolites. There were 5 genera which were not significantly correlated with any metabolites both in serum and feces, including Rikenella, Odoribacter, norank_f_Peptococcaceae, Parasutterella and Enterorhabdus, while Akkermansia and Alistipes were significantly correlated with at least five metabolites both in serum and feces. Moreover, as the results show, there was an obvious clustering phenomenon among the bacteria and metabolites. In serum (Figure 5A), specifically, Akkermansia were positively correlated with sphingolipids Cer(d18:0/17:0) and Cer(d18:0/15:0), but were negatively correlated with glycerophospholipids PC (3:0/3:0) and PS (16:0/18:0), while Alistipes and Mucispirillum made the opposite. Meanwhile in terms of faeces (Figure 5B), sphingolipids CerP 25:1, Cer-NDS d32:0, Cer-NS d34:5 and Cer 34:2 were found negatively correlated with Akkermansia, but were positively correlated with Alistipes. Whereas α-Linolenic acid and γ-Linolenic acid made the opposite. These findings revealed that the reduced abundance of Akkermansia and increased abundance of Alistipes were related to increasing levels of ceramides in APP/PS1 mice. When the abundances of Akkermansia and Alistipes were changed by ICA intervention, levels of ceramides fell in APP/PS1 mice. Together, these results indicate that the therapeutic effect of ICA may be strongly linked to sphingolipids metabolism and that Akkermansia and Alistipes may play a crucial role in this process, supporting the notion that the microbiome-metabolites-brains axis mediates the anti-AD effects of ICA.

3.5. Network pharmacology anticipated the potential drug-target pathway connection linked with the anti-AD activity of ICA.

Network pharmacological analysis was performed to fully understand the anti-AD mechanism of ICA. First and foremost, 184 identified drug targets were identified through PubChem, Pharm Mapper and Swiss Target Prediction. Meanwhile, 3630 AD-related genes were extracted from OMIM and GeneGard, and we screened out 115 prospective ICA targets for the therapy of AD (Figure 6 A, Supplement.table 1). As proteins engaged in biochemical processes generate supramolecular compounds to carry out biological activities, it is essential to investigate the association of ICA with various proteins in order to characterize its pharmacological effects. All 115 candidate targets of ICA were input into the STRING database (https://string-db.org/) to collect insight on protein interactions. The minimum score was set to the maximum confidence value of 0.9, and unconnected proteins were excluded from the network. A PPI network was constructed with 111 nodes (reflecting functional proteins) and 3,100 edges (reflecting the interactions between functional proteins and other proteins). The PPI network was built by Cytoscape 3.8.0 (https://www.cytoscape.org/) (Figure 6 C), and the top 25 anticipated hub genes, including TNF, AKT1, TP53, EGFR, and NFKB1, were ordered by the node degree (Figure 6 B). The findings showed that these core genes may be the most crucial target genes of ICA for treating AD.

KEGG enrichment analysis was employed to determine which pathways were enriched by 115 target genes (p‐value <0.05). A total of 119 KEGG pathways, including the PI3K-Akt signaling pathway, the sphingolipid signaling pathway, and the NF-κB signaling pathway, were considerably enriched (Supplement.table 2). Figure 6D depicts the bubble plot of the 15 most important KEGG pathways, and a drug–target–pathway network was constructed accordingly. Notably, we found that ICA had a regulatory effect on the sphingolipid signaling pathway via PRKCA/TNF/TP53/AKT1/RELA/NFKB1 (Figure 6E). The predicted genes were selected for molecular docking with the ICA.

The findings demonstrate that ICA may readily access and bind to the active pocket of PRKCA and five additional proteins (Supplement.fig 4). Combining the metabolitcs results, we speculate that ICA could affect the sphingolipid signaling pathway by PRKCA/TNF/TP53/AKT1/RELA/NFKB1.

As an active component of Epimedii, ICA has several therapeutic effects. Substantial evidence suggests that ICA may be advantageous for AD by lowering phosphodiesterase-5 activity, decreasing the formation of external Aβ plaques and intracellular neurofibrillary tangles[4, 9]. Therefore, ICA might be a promising AD medication. As revealed in our study, ICA remedied cognitive impairment and histopathological alterations in APP/PS1 mice, and concurred with other previous studies [22, 23]. Accordingly, we discovered that the beneficial effect of ICA in APP/PS1 mice is connected with restoring intestinal dysbiosis and metabolite profile, and both Akkermansia and Alistipes may significantly affect the modulation of sphingolipid metabolism. Our findings underscore the significance of the microbiome-microbiota-metabolites-brain axis in the anti-AD effect of ICA and indicate that modifying this axis is a promising therapeutic approach for the treatment of AD.

The gut connects the brain through neuroinflammation and the gut nervous system [24]. Microbiota dysbiosis occurs in patients with AD and changes in the abundance of particular species of intestinal flora have been linked to the progression of AD [25]. For example, Zhang et al. found that the abundance of Ruminiclostridium_5, which is related to systemic inflammation, was markedly elevated in the intestinal flora of mice with AD, and the transplantation of gut microbiome from control mice alleviated inflammatory responses [26]. Importantly, emerging evidence highlights the close interplay between ICA and the gut microbiota [9, 27].

Consistent with earlier research, our work reveals that ICA has a profound influence on the microbial makeup of the gut microbiome. ICA restored gut dysbiosis by downregulating the Firmicutes/Bacteroidetes ratio at the phylum level, which was a finding similar to previous studies [28]. At the genus level, the abundance of Alistipes associated with a higher-fat diet [20] increased in the AD disease state, but reverted after ICA treatment. On the contrary, the abundance of Akkermansia with anti-inflammatory effects and which supports a healthy metabolic status [19], was significantly elevated after ICA treatment.

Typically, the gut microbiome performs its activities through a complicated interplay with the host's metabolism. For example, the therapeutic effects of Akkermansia on the gut-brain axis include maintenance of the intestinal mucosal barrier and regulation of lipid metabolites [29]. Thus, it is plausible that changes in the makeup of the intestinal flora as a consequence of ICA-treatment will result in metabolic changes. In fact, our functional analysis of the intestinal microbiota and metabolomic profiling of feces demonstrated significant differences in the metabolic profiles of the intestinal flora between ICA-treated and vehicle-treated APP/PS1 animals. These alterations included a considerable decrease in sphingolipids. Ceramides are the primary metabolites and second messengers of sphingolipids, the up-regulation of which can increase Aβ by regulating the activity of β-secretase [30] and γ-secretase [31]. Through triggering sphingomyelinase, which catalyzes the breakdown of sphingomyelin to ceramides, the resultant oligomers and fibrous Aβ stimulate additional increases in ceramide levels, constituting a positive feedback cycle [32]. Furthermore, numerous pieces of evidence also highlight the impact of ceramides in neuronal death [33]. Specifically, levels of ceramides were decreased by ICA in our study, suggesting that ICA had positive effects on the composition and metabolomic profiling of gut microbiome. In addition, serum metabolomics further proved that ICA could, at least partially, regulate the disorder of lipid metabolism in the body.

Correlation analysis showed Akkermansia and Alistipes were observably correlated with at least five metabolites, both in serum and feces, and might be major gut microbiota that interacted with host metabolites. Alistipes is a comparatively novel sub-branch of the Bacteroidetes phylum, which is often linked with persistent intestinal inflammation [34]. Abnormal levels of Alistipes were associated with the onset of intestinal mucosal damage and lipid metabolism disorders[35]. Consistent with prior findings, our results observed that the abundance of Alistipes was dramatically correlated with sphingolipid metabolism[36]. On the contrary, it is believed that Akkermansia possesses anti-inflammatory qualities in the intestines and may reduce obesity and regulate lipid metabolism[37]. In AD, Akkermansia has been proved to promote the reduction of Aβ40-42 in APP/PS1 mice and correct intestinal barrier dysfunction and dyslipidemia[38]. In the present study, ICA therapy lowered ceramide levels in APP/PS1 mice, which was related to a rise in Akkermansia and a reduction in Alistipes. Collectively, our findings underscored the significance of the microbiome-metabolites-brain axis in mediating the preventive effects of ICA in the development of AD.

Finally, network pharmacology has been used to understand the relationships between drugs and organisms from the perspective of system networks. The results revealed that the therapeutic impact of ICA on AD may be implicated in a variety of signaling pathways including the PI3K-Akt signaling pathway, sphingolipid signaling pathway and NF-κB signaling pathway, consistent with previous studies [39]. In ovarian cancer, ICA has been proved to have a therapeutic effect via the PI3K-Akt signaling pathway and the NF-κB signaling pathway [40]. Consistent with the metabolomics results, ICA was speculated to affect the sphingolipid signaling pathway by PRKCA/TNF/TP53/AKT1/RELA/NFKB1 (Figure 6F). It is known that sphingomyelin and its metabolites serve as second messengers in a number of cellular signaling pathways. Specifically, ceramides, as sphingolipid metabolites, have been regarded as a novel class of powerful bioactive chemicals which could stimulate signaling pathways such as the PI3K/AKT and MAPK/ERK pathways, therefore regulating different processes associated with neuronal survival, and death [41]. Of particular note, TNF-α has been considered as an immune-mediated factor of the sphingolipid signaling pathway in AD [42] and associated with synaptic dysfunction in AD-related cognitive decline [43]. PRKCA, one of the ten members of the protein kinase C family, may be phosphorylated to stimulate the production of SPHK1 and SPHK2 to trigger ceramide-induced apoptosis [44]. RELA and NFKB1 commonly encode the subunit of protein NF-kappa B that is involved in the stimulation of the downstream NF-kappa B signaling pathway [45]. Phosphorylation of AKT1 is proved to induce the generation of ceramide synthases6, which catalyzes the formation of ceramides from sphingoid bases and acyl-CoA substrates through activation of the recombinant ceramide synthases6 promoter [46]. The p53 encoded by TP53 is a crucial part of the cell apoptosis induced by sphingolipids [47]. The affinity of these predicted proteins with ICA were all proved by molecular docking and thus, they all might be potential targets for ICA treatment of AD.

This is the first study to thoroughly explore the mechanisms accounting for the positive efficacy of ICA on AD by integrated 16s rRNA sequencing, multi-metabolomics and network pharmacology. Here, we have reported that ICA can effectively improve the cognitive impairment and pathologic changes caused by AD. In addition, our study revealed that ICA ameliorated the gut microbiome disturbance and metabolic disorder of APP/PS1 mice. Interestingly, there was a complex interaction between gut microbiota (Alistipes and Akkermansia) and sphingolipid metabolism, which might be key factors contributing to the therapeutical effect of ICA on AD. Network pharmacological analysis revealed the potential mechanism of ICA that might affect the sphingolipid signaling pathway by PRKCA/TNF/TP53/AKT1/RELA/NFKB1. Our results indicate that ICA is a viable medication for preventing the course of AD, at least through the microbiome-metabolites-brain axis, and represents a novel AD treatment path. However, further systematic molecular biology experiments are needed to explore the molecular processes associated with ICA.

16S rRNA 16S ribosomal RNA, AD Alzheimer's disease, ANOVA analysis of variance, COX cyclooxygenase, CYP cytochromeP, FC Fold change, GABA gamma-amino butyric acid, GO, Gene ontology; GSK Glycogen Synthase Kinase, HE Hematoxylin-eosin, ICA Icariin, KEGG, Kyoto Encyclopedia of Genes and Genomes, LDA Linear discriminant analysis, LEfSe, Linear discriminant analysis effect size, MWM Morris water maze, NCBI National Center for Biotechnology Information, NFT neurofibrillary tangle, NLRP6 Nucleotide-binding oligomerization domain, leucine- rich repeat and pyrin domain- containing 6, OPLS-DA orthogonal partial least-squares discriminant analysis, PCR Polymerase Chain Reaction, PLS-DA partial least squares discrimination analysis, silent mating type information regulation 2 homolog- 1 SIRT1, SRA Sequence Read Archive, tumor necrosis factor TNF, UHPLC/TOF-MS ultrahigh-performance liquid chromatography/time-of-flight mass spectrometry, VIP variable importance in the projection.

Ethics approval and consent to participate

The experiments complied with the ARRIVE guidelines and were carried out in accordance with National Institutes of Health guide for the care and use of Laboratory animals, and obtained an ethical review form regarding animal welfare approved by the Animal Ethics Committee of the Second Xiangya Hospital, Central South University (2020038).

Consent for publication

Not applicable.

Availability of data and materials

The dataset supporting the conclusions of this article is available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under BioProject ID: PRJNA918352.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work.

Funding

This work was financially supported by the National Natural Science Foundation of China (No. 81873169, 82104967), The science and technology innovation Program of Hunan Province (No.2022RC1220), China Postdoctoral Science Foundation (No.2022M711733), Hunan Provincial Natural Science Foundation of China (No. 2020JJ4803,2022JJ40728), Hunan Provincial College Students Innovation and Entrepreneurship Training Program support project (S2022105330440), and Hunan Flagship Department of Integrated Traditional Chinese and Western Medicine.

Author contributions

Weijun Peng: Conceptualization, Methodology. Yuqing Liu and Hongli Li: Data curation, Writing-Original draft preparation. Xiaowei Wang and Jianhua Huang: Visualization, Investigation. Di Zhao, Yejun Tan and Zheyu Zhang: Software, Validation. Zhen Zhang, Lemei Zhu, Beibei Wu and Zhibao chen: Writing-Reviewing and Editing. All authors contributed and approved the final version of the manuscript.

Acknowledgements

We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

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published 24 Apr, 2023

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Anti-Alzheimers molecular mechanism of icariin: Insights from gut microbiota and metabolome combined network pharmacology

Status:

Journal Publication

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Background

2. Methods

2.1 Animal modeling, grouping, and intervention

2.2. Morris Water-Maze (MWM) test

2.3. Hematoxylin and eosin (HE) staining

2.4. 16S rRNA Gene Sequencing analysis

2.4.1 Collection of intestinal contents

2.4.2. Gut data acquisition and processing

2.5.1 Sample collection and preparation

2.5.2 LC-MS data acquisition and processing

2.6. Gut microbiota-metabolite correlation analysis

2.7. Network pharmacology

2.8. Statistical analysis

3. Results

3.1. ICA alleviated cognitive impairment and histopathological changes in APP/PS1 mice

3.2. ICA altered the composition of gut microbiota in APP/PS1 mice

3.3. ICA reversed fecal metabolomics disorder in APP/PS1 mice

3.4. ICA reversed serum metabolomics disorder in APP/PS1 mice

3.4. Interaction between gut microbiota and metabolites

3.5. Network pharmacology anticipated the potential drug-target pathway connection linked with the anti-AD activity of ICA.

4. Discussion

Conclusions

abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1