Exploring the Mechanism of Laminaria for the Treatment of Alzheimer's Disease based on Network Pharmacology

Background (cid:0) Laminaria japonica has also been reported to have a therapeutic effect on AD, but the mechanism is not entirely clear. To explore the mechanism of Laminaria for the treatment of Alzheimer's disease (AD), the “active components-targets” network and the protein-protein interaction (PPI) network were constructed for analyzing targets’ functions and pathways. Methods (cid:0) The main active components of Laminaria were extracted using the TCMSP database and were predicted and screened by GeneCards. Cytoscape was used to construct the “drug-components-targets-disease” network. STRING and Cytoscape were applied to map the PPI network. The Gene Ontology (GO) terms and KEGG pathways of targets were analyzed by Metascape. Results: Seven active components involving 23 active targets were obtained. The network analysis elucidated that Laminaria was mainly involved in cell process, metabolic process, response to stress and other biological processes. CASP3, PPARG, RELA, CCND1 and CASP9 played a key role in treating AD by regulating two small cell lung cancer and Toxoplasmosis. Conclusion: This study demonstrated that Laminaria could prevent and treat AD with advantages of multi-components, multi-targets and multi-pathways, which explored a new way for further research on the mechanism of Laminaria in the treatment of AD. molecular mechanism of the potential targets of Laminarin in the treatment of AD. Relevant target verication experiments are in progress.


Background
Alzheimer's disease (AD), characterized by impairment of memory, cognitive dysfunction and social disorders, is a chronic neurodegenerative disease [1]. According to Alzheimer's Disease International, dementia affects 50 million people worldwide, with a new case of dementia occurring somewhere in the world every 3 seconds. The symptoms of the disease is most common in those aged 60 or older and the neurologic changes caused by AD are irreversible [2]. The aging of the global population is unprecedented. The number of people over 60 in the world is projected to increase by 56% between 2015 and 2030, and by 2050 the global elderly population is projected to more than double. Thus, age remains the greatest risk factor for AD. The pathological hallmarks of AD include amyloid β-protein deposition [3], abnormal phosphorylation of the protein tau [4], neuroin ammatory response [5], cholinergic de cit [6], oxidative stress [7], et al. At present, medicine for treatment of AD can only relieve the symptoms and have relatively large side effects, which cannot repair nerve damage and prevent the deterioration of the disease [8][9][10].
Therefore, it is urgent to systematically elucidate the mechanism of AD and nd safe and effective agents against AD.
Laminaria japonica, the most common member of the brown algae family, is not only well-known as "longevity food", but also as traditional Chinese herbal medicine that is used to prevent and treat various diseases for over a thousand years [11]. Laminaria is rich in vitamins, minerals, dietary ber, proteins and polysaccharides [12], which has shown to possess many biological activities such as antibacterial [13], antiviral [14], anti-in ammatory [15], anti-tumor [16], anti-diabetes [17] and antioxidant [18] and protective effects against liver damage, hypertension, obesity, insomnia [19]. Recently, Laminaria has also been reported to have a therapeutic effect on AD, but the mechanism is not entirely clear.
Network pharmacology is an emerging approach to explore the relationship between drugs and diseases that integrates system biology, network analysis, bioinformatics and multi-directional pharmacology [20]. It devotes to understand the drug's pharmacological mechanism and development in the network perspective. Network pharmacology can quickly and e ciently analyzes the mechanism of traditional Chinese medicine in a modern way [21]. In this study, the "drugs-components-targets-disease" network was constructed to analyze the relationship between components of Laminaria and AD-related target proteins to investigate the binding a nity and predict the possible binding sites of drugs. This strategy could provide a reasonable basis for further clinical and experimental research in Laminaria's action mechanism against AD.

Collection and selection of main chemical components
The chemical components of Laminaria were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and analysis platform (http://tcmspw.com/tcmsp.php) [22]. Oral bioavailability (OB) is an important pharmacokinetic parameter in drug absorption, distribution, metabolism and excretion, indicating the rate and degree of systemic absorption of active ingredients in oral drugs [23]. The parameters of OB ≥ 30% are considered to be absorbed and utilized by the body.
Drug-likeness (DL) is a necessary condition for preparing compound medicine, the value of which indicates the similarity between the ingredients and known chemical drugs. DL ≥ 0.18 is generally considered as an important reference for the activity [24]. According to the recommended criterion in TCMSP database, OB ≥ 30% and DL ≥ 0.18 were used to select the active components.

Collection of potential targets for Alzheimer's disease
The targets of potentially active components were collected in the TCMSP database. The collected targets were imported into the Uniprot database (http://www.uniprot.org/) by name with the "Homo sapiens" setting to obtain human-related protein targets. Different ID types of the proteins were converted to UniProt IDs. The GeneCards (https://www.genecards.org/) database was used to obtain the gene name of each target by searching the keyword "Alzheimer's Disease" [25].

Construction of the "drug-targets-disease" interaction network
The targets of active components of Laminaria and the targets of AD were imported into a website called Bioinformatics & Evolutionary Genomics (http://bioinformatics.psb.ugent.be/webtools/Venn/), and the overlapping targets were collected. The target-disease and component-target network was created and merged to construct the component-target-disease network model using Cytoscape 3.7.0 software [26].
Construction of PPI network STRING 10.5 (https://string-db.org/) is a database of known and predicted protein-protein interactions including direct (physical) and indirect (functional) associations [27]. The AD-related common targets and the potential targets of Laminaria were applied to construct the PPI network using STRING 10.5 database with the "Homo sapiens" setting to achieve a comprehensive understanding of the relationships among compounds, targets, and AD. The STRING automatically scores each PPI, and the higher the score, the higher the con dence. In this study, a PPI network consisting of the products of gene expression was constructed based on the 30 data with top scores.

Analysis of targets' pathways
To further understand the function of targets' application in the signal pathway, the Laminarin-AD overlapping targets were introduced into the Metascape database (https://metascape.org/), in which these targets were standardized under the "Homo sapiens" setting and the threshold was set as P ≤ 0.05. Gene Ontology (GO) terms [28] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [29] pathways were analyzed. The pathway map of the effect of Laminaria on AD was constructed by KEGG Mapper (https://www.kegg.jp/). GraphPad Prism 5.0 was used for mapping.

Screening of active compounds
A total of 48 compounds in Laminaria were obtained from TCMSP. Based on the absorption, distribution, metabolism, excretion calculation, 7 active compounds with OB ≥ 30% and DL ≥ 0.18 were screened. The information of the 7 active compounds in Laminaria was shown in Table 1. Identi cation of the targets of Laminaria on AD A total of 72 protein targets were collected based on the above 7 active compounds. 34 AD-related targets were obtained when the 72 targets were mapped to the UniProt database for normalizing and standardizing naming. There are 8,718 AD-related targets in the GeneCards database. The targets of drug components were compared with that of AD and 23 potential targets related to the treatment of AD with Laminaria were selected ( Table 2). Construction and analysis of the "drug-components-targetsdisease" network Information about Laminaria's active components and overlapping targets was imported into Cytoscape 3.7.0 to establish a "drug-components-targets-disease" visualization network. As shown in Figure 1, the purple node represented the drug thallus Laminariae, the yellow nodes represented the active components, the green nodes represented overlapping genes between Laminaria and AD, and the pink node represented the disease AD. The same target corresponded to different active components, and vice versa, which su ciently suggested Laminaria's characteristics, multi-components and multi-targets.

Construction and analysis of the PPI network
The PPI network was constructed when the target proteins were introduced into the STRING database and their names were standardized under the "Homo sapiens" setting. As shown in Table 2, one node represented one protein, and the edge between the two nodes indicated the interaction between proteins. It was speculated that CASP3, PPARG, RELA, CCND1, CASP9 were the key targets for the treatment of AD with Laminarin.

Analysis of GO and pathway of targets
GO and KEGG analyses were performed on the targets of active components for the treatment of AD using the Metascape database. The threshold P ≤ 0.05 was set to select biological processes and pathways. The GO provides the logical structure of the biological functions, including three aspects: biological process, molecular function and cellular component, and how these functions are related to each other, manifested as a directed acyclic graph. Figure 3 showed the results of GO analysis for the predictive targets of the effect of Laminarin on AD, the response to steroid hormone accounted for the largest proportion in the biological process, platelet dense granule was the only one in cellular component, and steroid binding, steroid hormone receptor activity, nuclear receptor activity, transcription factor activity and direct ligand regulated sequence-speci c DNA binding were at the top in molecular function.
KEGG was used to analyze the distribution of pathways for predicting the targets of Laminarin for AD. As shown in Figure

Discussion
Alzheimer's disease is the 5th leading cause of death among people aged 60 years or older and no viable method has been found to prevent and cure AD. Laminaria has been reported to have a therapeutic effect on AD, but the mechanism is not entirely clear. Thus, the study of the Laminaria mechanism on AD is of great signi cance. Network pharmacology is an emerging area of pharmacology that utilizes network analysis of drug action. By considering drug actions in the context of the cellular networks, network analysis promises to greatly increase our knowledge of the mechanisms underlying the multiple actions of drugs. In this study, the possible mechanism of the treatment of AD with Laminaria was analyzed by network pharmacology.
Through database searching and screening, 7 active components of Laminaria were obtained, and 23 overlapping targets of active components and AD were collected. Through the construction and screening of the "drug-components-targets-disease" network, Saringosterol, Eckol, Eicosapnte macnioc acid, 24-Methylenecholesterol, arachidonic acid, CLR, Fucosterol, et al. were predicted to be the active components in the treatment of AD by Laminaria.
CASP3, PPARG, RELA, CCND1 and CASP9 were identi ed as the key genes for the treatment of AD by the PPI network. The target network indicates the characteristics of Laminarin, multi-components and multipathways. The PPI network shows that there is an interactional and complex relationship among the Laminarin target proteins. The results of GO analysis showed that the mechanism of Laminaria on AD involved biological processes like cellular processes, metabolic processes and responses to stress, cellular components such as organelles, cell membranes and cytoplasms, molecules like small molecules, cations and metal ions, and signal molecules, transcription factors, receptors, proteins, enzymes and other substances. The analysis on the target pathway showed that the main targets of Laminaria for AD were small cell lung cancer and toxoplasmosis.

Conclusions
The results suggest that Laminaria may act on multiple targets and thus play an anti-AD role. At present, there are few reports about these targets. This study may provide a new perspective for further study on the molecular mechanism of the potential targets of Laminarin in the treatment of AD. Relevant target veri cation experiments are in progress.

Declarations
Ethics approval and consent to participate Not applicable Consent for publication Not applicable Figure 1 The "drug-components-targets-disease" visualization network.

Figure 2
The PPI network of Laminarin.

Figure 3
Enriched gene ontology terms for biological processes, cellular components and molecular functions of potential targets from the main active components of Laminarin.

Figure 4
Enriched KEGG pathways of potential targets from the main active components of Laminarin.

Figure 5
Signal pathway of small cell lung cancer.

Figure 6
Signal pathway of toxoplasmosis.