Abstract

   In recent years, the Alzheimer’s disease (AD) epidemic has become one
   of the largest global healthcare crises. Besides, the available
   systemic therapies for AD are still inadequate. Due to the insufficient
   therapeutic options, new treatment strategies are urgently needed to
   achieve a satisfactory therapeutic effect. Marine bio-resources have
   been accepted as one of the most economically viable and sustainable
   sources with potential applications for drug discovery and development.
   In this study, a marine cyanobacteria–Synechococcus sp. XM-24 was
   selected as the object of research, to systematically investigate its
   therapeutic potential mechanisms for AD. The major active compounds
   derived from the Synechococcus sp. biomass were identified via
   pyrolysis-gas chromatography-mass spectrometry (GC-MS), and 22
   compounds were identified in this strain. The most abundant chemical
   compounds was (E)-octadec-11-enoic acid, with the peak area of 30.6%.
   Follow by tridecanoic acid, 12-methyl- and hexadecanoic acid, with a
   peak area of 23.26% and 18.23%, respectively. GC-MS analysis also
   identified indolizine, isoquinoline, 3,4-dihydro- and Phthalazine,
   1-methyl-, as well as alkene and alkane from the strain. After the
   chemical toxicity test, 10 compounds were finally collected to do the
   further analysis. Then, network pharmacology and molecular docking were
   adopted to systematically study the potential anti-AD mechanism of
   these compounds. Based on the analysis, the 10 Synechococcus-derived
   active compounds could interact with 128 related anti-AD targets. Among
   them, epidermal growth factor receptor (EGFR), vascular endothelial
   growth factor A (VEGFA) and mitogen-activated protein kinase 3 (MAPK3)
   were the major targets. Furthermore, the compounds N-capric acid
   isopropyl ester, (E)-octadec-11-enoic acid, and 2H-Pyran-2,4(3H)-dione,
   dihydro-6-methyl- obtained higher degrees in the compounds-intersection
   targets network analysis, indicating these compounds may play more
   important role in the process of anti-AD. In addition, Kyoto
   Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed
   that these active compounds exert the anti-AD effects mainly through
   PI3K-Akt signaling pathway, neuroactive ligand-receptor interaction and
   ras signaling pathway. Our study identified Synechococcus-derived
   bioactive compounds have the potential for application to AD by
   targeting multiple targets and related pathways, which will provide a
   foundation for future research on applications of marine cyanobacteria
   in the functional drug industry.

   Keywords: cyanobacteria, marine Synechococcus sp., gas
   chromatography-mass spectrometry, volatile organic compounds,
   Alzheimer’s disease, network pharmacology

1 Introduction

   Recently, cyanobacteria (blue-green algae) have been accepted as one of
   the economic and viable sources with potential applications in various
   fields, including food, nutraceutical, therapeutics, pharmaceutical,
   cosmetics and personal care industries ([38]Bourgade and Stensjö, 2022;
   [39]Pathania et al., 2022; [40]Bishoyi et al., 2023). They are an
   extremely diverse group of photosynthetic prokaryotes, owing to their
   adaptive capacity to tolerate extreme conditions, cyanobacteria are
   found in almost all the habitats of the Earth, from desert crusts to
   open ocean ([41]Ferris et al., 1996; [42]Nubel et al., 1999;
   [43]Garcia-Pichel and Pringault, 2001; [44]Gaysina et al., 2019). A
   variety of natural ingredients are reported to be found in
   cyanobacteria, including vitamins, proteins, polysaccharides, lipids,
   sterols, enzymes as well as other fine chemicals ([45]Dahms et al.,
   2006; [46]Abed et al., 2009; [47]de Jesus Raposo et al., 2013;
   [48]Hassan et al., 2022). The metabolic versatility of cyanobacteria
   lends them to bioengineering applications for sustainable production of
   various products ([49]Stal, 2007; [50]Xie et al., 2017; [51]Momper et
   al., 2019; [52]Liu et al., 2021). Especially, cyanobacteria derived
   bioactive compounds have a development potential in scientific research
   and great opportunity for drug discovery ([53]Vijayakumar and Menakha,
   2015; [54]Sahoo et al., 2021). For example, apratoxin D isolated from
   cyanobacterium Lyngbya sp. has exhibited powerful cytotoxicity to human
   lung cancer cells ([55]Gutierrez et al., 2008). Additionally,
   Symplocamide A, produced by marine cyanobacterium Symploca sp. has
   shown strong cytotoxicity to neuroblastoma as well as lung cancer cells
   ([56]Linington et al., 2008). Cyanobacteria are also reported to be a
   rich source of omega-3 fatty acids, including eicosapentaenoic acid
   (EPA) and docosahexaenoic acid (DHA), which have the potential to
   prevent inflammatory and cardiovascular diseases ([57]Sijtsma and de
   Swaaf, 2004; [58]Udayan et al., 2017; [59]Priyadarsini et al., 2022).
   Furthermore, cyanobacteria derived compounds could not only be applied
   directly as drugs, but also be used as potential lead compounds for new
   drug discovery ([60]Tan L. T. 2007). For example, as potential
   anticancer drugs, curacin A from cyanobacteria Lyngbya majuscule and
   dolastatin 10 from cyanobacteria Symploca sp., have been in preclinical
   and entered into clinical trials, respectively. Moreover, these
   components also served as drugs leading to the discovery of synthetic
   analogues, such as TZT-1027, compound 4, LU-103793, and ILX-651,
   usually holding improved pharmacological and pharmacokinetic properties
   for the treatment of multiple types of cancers ([61]Uzair et al.,
   2012). Furthermore, cyanobacteria could also applied in the field of
   nanotechnology ([62]Mandhata et al., 2022, [63]Bishoyi et al., 2023).
   Sahoo et al. synthesized silver-nanoparticles (AgNPs) with
   cyanobacterium Nostoc sphaeroides and explored their antibacterial
   activity against bacterial strains that could lead urinary tract
   infection ([64]Sahoo et al., 2021).

   In the ocean, most species of picoplanktonic cyanobacteria currently
   known belong to two genera: Prochlorococcus and Synechococcus
   ([65]Palenik et al., 2003). They are the most abundant photosynthetic
   organisms on Earth, numerically dominating most oceanic waters
   ([66]Scanlan et al., 2009). Compared to Prochlorococcus, Synechococcus
   has a lower abundant in very oligotrophic waters, but has a broader
   global distribution, ranging from polar through temperate to tropical
   environments ([67]Flombaum et al., 2013). For example, in some coastal
   areas, they could account for 20% of the local primary productivity
   ([68]Li, 1994; [69]Tai and Palenik, 2009). Due to the great deal of
   genetic and metabolic versatility within the Synechococcus sp., with
   strains probably adapted to complex and specific ecological
   environments ([70]Scanlan et al., 2009; [71]Bourgade and Stensjö,
   2022). Besides, due to better adaptation to eutrophic environment,
   marine Synechococcus sp. is relatively easy to culture in the
   laboratory ([72]Ahlgren and Rocap, 2006). Compared to Prochlorococcus,
   Synechococcus require less care and exhibit a faster growth rate,
   making them great candidates as bioproducers ([73]Partensky et al.,
   1999; [74]Yu J. et al., 2015; [75]Ungerer et al., 2018). It has been
   reported that marine Synechococcus sp. contain multiple biologically
   active compounds, which include antibacterial, antifungal, antiviral,
   and cancer-fighting compounds ([76]Martins et al., 2008; [77]Costa et
   al., 2013; [78]do Amaral et al., 2020; [79]Srimongkol et al., 2023).
   Numbers of studies have indicated that they also have antioxidant and
   anti-inflammatory potential ([80]de Morais et al., 2015; [81]Gómez et
   al., 2023). For instance, Phycocyanin is a pigment found in blue-green
   algae, such as Synechococcus sp. ([82]Hsieh-Lo et al., 2019). It has
   reported has an antioxidant activity and anti-inflammatory properties
   that works through the inhibition of histamine release ([83]Romay et
   al., 1998; [84]Bhat and Madyastha, 2001; [85]Tan H. T. et al., 2022).
   As is well known, oxidative stress and chronic inflammation plays an
   important role in the progression of neurodegenerative diseases, such
   as Parkinson’s disease (PD), Alzheimer’s disease (AD), and multiple
   sclerosis (MS) ([86]Pangestuti and Kim, 2011). As the various
   biologically potential of Synechococcus-derived active compounds, such
   as antioxidant and anti-inflammatory, do they also have anti-AD
   potential? At present, the knowledge on the anti-AD potential of the
   substances derived from marine Synechococcus sp. is still scarce, as
   well as the pharmacological mechanism.

   AD, one of the major cause of dementia, is a degenerative brain disease
   characterized by the aggregates of extracellular β-amyloid (Aβ) plaques
   and intracellular tau protein, leading to neurofibrillary tangles,
   neuronal loss and cell death ([87]Parihar and Hemnani, 2004;
   [88]Citron, 2010). AD derived dementia is characterized by impairment
   in memory, language, problem-solving and other cognitive domains that
   affect a person’s ability to perform daily life activities ([89]Kim K.
   et al., 2020). Until now, Acetylcholinesterase inhibitors (AChEIs) are
   remain the primary therapy for AD patients with mild-to-moderate
   cognitive impairment ([90]Yu B. et al., 2022). However, these drugs do
   not have significant disease-modifying effects, but only provide
   symptomatic relief ([91]Yeo-Teh and Tang, 2023). Hence, an efficient
   strategy is urgently needed for AD therapy, and a novel strategy is
   needed for anti-AD drugs discovery. Due to the diversity of species and
   metabolism, as well as unique advantages such as efficient
   photosynthesis and carbon sequestration, active compounds derived from
   microalgae provide us with new directions ([92]Chen et al., 2022,
   Pathaniaet al. 2022). For example, a newly developed anti-AD
   drug‒sodium oligomannurarate, also called GV-971, was derived from
   marine algae, which can eventually accelerate Aβ clearance ([93]Wang X.
   et al., 2019). Recently, network pharmacology strategy has proven to be
   powerful in explaining the molecular mechanisms of natural medicine,
   such as traditional Chinese medicine and marine natural products, which
   has complicated ingredients and multiple targets ([94]Hopkins, 2007;
   [95]Wang S. et al., 2022; [96]Tikhonova et al., 2023). In addition, it
   could also serve as the basis for the novel drugs discovery. For
   example, Zeng et al. had used network pharmacology approach to uncover
   the key ingredients in Ginkgo Folium, as well as their anti-AD
   mechanisms ([97]Zeng et al., 2021a).

   In this study, we identified the organic compounds derived from marine
   cyanobacteria Synechococcus sp. XM-24 strain via pyrolysis-gas
   chromatography-mass spectrometry (GC-MS). Then, we evaluated the
   anti-AD potential of these compounds by applying network pharmacology
   strategy and molecular docking analysis. The study flow chart is shown
   in [98]Figure 1. It is hoped that our study will allow further insights
   into the exploration of active compounds derived from marine
   cyanobacteria.

FIGURE 1.

   [99]FIGURE 1
   [100]Open in a new tab

   Flow chart of a network pharmacology-based strategy to investigate
   marine Synechococcus sp. derived compounds for application to
   Alzheimer’s disease.

2 Materials and methods

2.1 Synechococcus culture and biomass harvesting

   Marine Synechococcus sp. XM-24 was used in this study. Synechococcus
   sp. XM-24 was isolated from the coastal region near Xiamen Island,
   China, and belonged to clade CB5 in subcluster 5.2 ([101]Zheng Q. et
   al., 2018). The strain were grown in the SN medium at 22°C under
   constant cool white light. The light intensity was 20–30 μE m^−2 s^−1.
   Synechococcus cells were collected via centrifugation (6000×g for
   25 min) at the exponential phase (∼15 days). To harvest
   Synechococcus-derived active compounds, the Synechococcus sp. XM-24
   biomass was distributed in falcon-tube, and cells were subjected to
   freeze‒thaw cycles, then freeze‒dried, and kept frozen (–20°C) until
   further processing.

2.2 GC‒MS analysis

   The pyrolysis-gas chromatography-mass spectrometry (GC‒MS) was applied
   to analysis the chemical composition of Synechococcus sp. XM-24 biomass
   as described by Yao Zhang et al. ([102]Zhang et al., 2016).
   Synechococcus sp. cells were moved into a quartz tube. Then, the
   biomass were moistened with tetramethylammonium hydroxide solution
   (100μL, 25% in methanol). The sample was pyrolyzed using a single point
   pyrolyzer (Frontier Laboratories Ltd., Japan) at 550°C, interfaced
   directly to a GC‒MS (QP 2010, Shimadzu, Japan). About the stability of
   the temperature, the room temperature changed by 1°C, the change of the
   in-cylinder temperature was less than 0.01°C. The products were
   automatically injected into the gas chromatograph. Helium (99.999%) was
   used as a carrier gas. The flow rate was 1.8 ml min^−1. The column
   temperature was set at 40°C for 3 min, and increased to 300°C at a rate
   of 10°C min^−1 for 15 min. The mass spectrometer was operated at an
   ionization energy of 70 eV (EI). The mass range for detection was
   25–500 m/z with a cycle time of 1s. Then, National Institute of
   Standards and Technology Mass Spectral Library database (NIST) was used
   for compound identification by comparing the obtained mass spectra with
   the database.

2.3 Potential targets of Synechococcus-derived active compounds

   The chemical toxicity test was performed through PubChem databases
   ([103]https://pubchem.ncbi.nlm.nih.gov/search/search.cgi), and all
   toxic compounds were eliminated. Then, the Swiss Target Prediction
   ([104]https://www.swisstargetprediction.ch/) ([105]Daina et al., 2019)
   and Search Server (SEA) ([106]https://sea.bkslab.org/) ([107]Keiser et
   al., 2007) were used to identify the potential targets of all these
   Synechococcus-derived active compounds.

2.4 Potential targets database of Anti-AD

   GeneCards ([108]https://www.genecards.org/) ([109]Rebhan et al., 1998)
   and DisGeNET ([110]https://www.disgenet.org/) ([111]Pinero et al.,
   2017) were employed to screen potential genes, by setting “Alzheimer’s
   disease” and “anti-Alzheimer’s disease” as the keyword. Then, after
   further processing and removing the duplicate targets, the final
   targets related to anti-AD were collected. In addition, both of
   collected compound targets and anti-AD targets were imported into the
   InteractiVenn online platform ([112]http://www.interactivenn.net/) to
   draw a Venn graph. Venn diagrams were employed to determine the
   intersection targets between Synechococcus-derived active compounds and
   anti-AD.

2.5 Active compound‒intersection target network construction

   Cytoscape software was used to build a visualization network graph of
   active compound-intersection targets. Firstly, the obtained main active
   compounds and the intersection targets were imported into the Cytoscape
   3.9.1 software. A visualization network diagram was generated from this
   software. Then, the Network Analyer plugin in Cytoscape 3.9.1 was used
   to analyze the topology parameters of this network. Based on degree,
   betweenness, and closeness, hub nodes were identified ([113]Yu B. et
   al., 2022).

2.6 Protein-protein interaction network construction

   The STRING database ([114]https://string-db.org/) was used to construct
   a protein–protein interaction (PPI) network for the activities of
   Synechococcus-derived active compounds against AD. In this study, PPI
   can help us understand the functions and relationships of key target
   proteins ([115]Athanasios et al., 2017). The STRING database can
   collect and integrate data about known and predicted protein‒protein
   associations, including both direct and indirect interactions, from
   many organisms ([116]Szklarczyk et al., 2017). On STRING website, the
   main targets were imported into the “Multiple proteins” option, and
   “Homo sapiens” was selected in the organism query. And other parameters
   was established as the “default” criterion. Then, the visualized PPI
   network map was established using Cytoscape software ([117]Shannon et
   al., 2003).

2.7 GO functional analysis and KEGG enrichment pathway analysis

   To further explore the pathways of the anti-AD, the intersection
   targets were entered in the DAVID database
   ([118]https://david.ncifcrf.gov/) ([119]Huang da et al., 2009). Gene
   Ontology (GO) enrichment analysis is composed of biological process
   (BP), cellular component (CC), and molecular function (MF). Kyoto
   Encyclopedia of Genes and Genomes (KEGG) is a knowledge database which
   could be used to describe the interaction between different molecules
   and reveal known metabolic pathways ([120]Chen J. et al., 2015). These
   two analysis helped to figure out the function of the core targets and
   the critical pathways of Synechococcus-derived active compounds against
   AD. The intersection targets data were imported into the DAVID
   database, and GO and KEGG analyses were performed on these intersection
   targets. Additionally, KEGG pathway enrichment analysis was also
   performed using ClueGO plug-in in Cytoscape software.

2.8 Bioinformatic analysis of intersection targets correlated with Aβ and Tau
pathology

   To study the pathological correlation between Synechococcus-derived
   ingredients with Aβ and tau in the treatment of AD, the intersection
   targets were imported into the AlzData database
   ([121]http://www.alzdata.org/index.html) to perform the correlation
   analysis. The results were visualized through the Hiplot online
   platform ([122]https://hiplot.com.cn/cloud-tool/drawing-tool/list). For
   convenience observation, the analysis were magnified by 100 times. The
   targets related to Aβ, tau and both of Aβ and tau were constructed PPI
   network using STRING database and Cytoscape software, to identify the
   core targets most correlated with Aβ and tau.

2.9 Molecular docking

   The AutoDock software Vina was used to molecular docking simulation,
   revealing the binding modes and the strength between
   Synechococcus-derived active compounds and their protein targets
   ([123]Morris and Lim-Wilby, 2008). The two-dimensional structures of
   the main active compounds of Synechococcus sp. XM-24 were collected
   through PubChem database. The 3D coordinates of KDR (PDB ID, 5EW3;
   resolution, 2.5 Å) and BIRC5 (PDB ID, 4AOI; resolution, 1.9 Å) were
   downloaded from the Protein Data Bank
   ([124]http://www.rcsb.org/pdb/home/home.do) ([125]Burley et al., 2021),
   and the chemical structures of the main active compounds were obtained
   from the TCMSP platform. The active pockets in the 3D structure of the
   proteins were identified by AutoDock Vina Tools, after removing the
   water molecules and small molecule ligands water molecules from the
   protein, and adding hydrogen. Then, using the prepared protein models
   and ligand structures, molecular docking was conducted through the
   AutoDock Vina software. The lower the score of the docking results, the
   stronger the binding ability. The molecular docking results were
   displayed in two-dimensional (2D) and three-dimensional (3D) graphics.

2.10 Molecular dynamics simulation

   Proteins and compounds were parameterized using AMBER ff19SB and GAFF
   force fields, respectively ([126]Wang et al., 2006; [127]Tian et al.,
   2020). The system was solvated using an OPC water model and a truncated
   octahedral water box with a cutoff of 10 Å. Periodic boundary
   conditions (PBCs) were applied, and the system was neutralized.
   Non-bonded van der Waals interactions were calculated using the
   Lennard‒Jones 12–6 potential with a cutoff of 10 Å, while long-range
   electrostatic forces were treated using the PME algorithm. The SHAKE
   algorithm was used to constrain covalent bonds involving hydrogen atoms
   ([128]Ryckaert et al., 1977). To eliminate steric clashes, heavy atoms
   were initially subjected to a constraint force of 10 kcal/(mol·Å) and
   underwent energy minimization using a combination of 2,500 steps of
   steepest descent and 2,500 steps of conjugate gradients. The system was
   then relaxed through 10,000 steps of steepest descent and 10,000 steps
   of conjugate gradient energy minimization. Subsequently, a 20 ps NVT
   simulation was performed to gradually heat the system to 300 K. This
   was followed by two equilibration phases: a 200 ps restrained heavy
   atoms NPT simulation and a 500 ps unrestrained NVT simulation. The
   temperature was controlled at 300 K using a Berendsen thermostat, and
   the pressure was maintained at 1 atm using a Monte Carlo barostat with
   coupling constants and relaxation times set as 1 ps. Finally, a 100 ns
   NVT simulation was carried out with a time step of 2 fs. RMSD and RMSF
   analyses were conducted using the CPPTRAJ module ([129]Roe and
   Cheatham, 2013).

3 Results

3.1 GC-MS analysis

   GC-MS analysis identified twenty-two chemical compounds from
   Synechococcus sp. XM-24 biomass. The identification of Synechococcus sp
   derived compounds is based on the mass spectra, including the retention
   time and peak area, and comparing with NIST database. All substances
   information are shown in [130]Supplementary Figure S1 and
   [131]Supplementary Table S1 in the [132]Supplementary Material. The
   major types of compounds are fatty acids, alkene, alkane and esters.
   The most abundant chemical compound from Synechococcus sp. XM-24 was
   (E)-octadec-11-enoic acid, with the peak area of 30.6%. The second most
   abundant compound was tridecanoic acid, 12-methyl-, with a peak area of
   23.26%. Follow by hexadecanoic acid, with a peak area of 18.23%. These
   three compounds together accounted for 72.09% of the peak area of the
   GC-MS. GC-MS analysis also identified indolizine, isoquinoline,
   3,4-dihydro- and phthalazine, 1-methyl-, from the strain XM-24. In
   contrast, their proportions were not very high.

3.2 Potential targets of the main compounds derived from Synechococcus sp

   We performed a chemical toxicity test on the twenty-two compounds
   though PubChem databases. The result showed that some components have
   cell toxicity and neurotoxicity, such as isoquinoline, 3,4-dihydro-,
   benzenepropionitrile, indolizin and so on. We did not do further
   analysis of these compounds in this study. Finally, ten compounds were
   collected as candidates to do the further analysis ([133]Table 1).
   Then, by searching multiple online databases, including Swiss Target
   Prediction and SEA, 354 potential targets of the ten compounds were
   identified.

TABLE 1.

   Profile of the key compounds.
   R.Time Time Area% Entry CAS CompName Smiles
   7.147 7.147 1.92 7039 85825-79-2 2H-Pyran-2,4(3H)-dione,
   dihydro-6-methyl- CC1CC(=O)CC(=O)O1
   14.3 14.299 1.43 12,814 7045-71-8 Undecane, 2-methyl- CCCCCCCCCC(C)C
   10.443 10.445 1.36 3688 15870-10-7 1-Heptene, 2-methyl- CCCCCC(=C)C
   13.413 13.415 1.19 7987 5004-46-6 Phthalazine, 1-methyl- CC1 = NN = CC2
   = CC = CC = C12
   16.747 16.746 1.08 19,392 2311-59-3 N-capric acid isopropyl ester
   CCCCCCCCCC(=O)OC(C)C
   9.283 9.284 0.93 4359 589–18-4 Benzenemethanol, 4-methyl- CC1 = CC =
   C(C=C1)CO
   13.293 13.293 0.87 10042 821-97-6 3-Undecene, (Z)- CCCCCCCC = CCC
   11.203 11.203 0.8 3526 14289-96-4 Allyl methallyl ether CC(=C)COCC = C
   14.237 14.236 0.8 10043 1002-68-2 3-Undecene, (E)- CCCCCCCC = CCC
   17.733 17.733 30.6 115401 693-72-1 (E)-octadec-11-enoic acid CCCCCCC =
   CCCCCCCCCCC(=O)O
   [134]Open in a new tab

3.3 Anti-AD targets of main compounds derived from Synechococcus sp

   A total of 2,297 and 853 potential anti-AD targets were obtained by
   searching the GeneCards and DisGeNET databases, respectively. After
   removing duplicate targets, 2,691 potential anti-AD targets were
   finally collected. By employing the InteractiVenn online platform, the
   354 predicted targets of the Synechococcus-derived active compounds and
   the 2,691 AD-related disease targets were used to construct the Venn
   diagram ([135]Figure 2). The results showed that a total of 218
   intersection targets of active compounds with potential anti-AD effects
   were obtained. These intersection targets were used to do the
   subsequent analysis.

FIGURE 2.

   [136]FIGURE 2
   [137]Open in a new tab

   The intersecting targets between potential and AD-related targets.
   Pink: potential targets of active compounds; blue: targets of AD;
   purple: intersecting targets.

3.4 Active compound-intersection target network

   The network analysis results are shown in [138]Figure 3. Finally, 10
   Synechococcus-derived active compounds and 218 potential anti-AD
   targets were imported into the Cytoscape software for construction of
   visual network. network analysis revealed 139 nodes with 304 edges,
   with an average node degree of 4.374. Nodes represented the different
   active compounds and intersection targets. The connections between
   nodes represent the interactions between active compounds and their
   targets. As shown in [139]Figure 3, most the active compounds could
   affected multiple targets, particularly compounds N-capric acid
   isopropyl ester and (E)-octadec-11-enoic acid. Moreover, the larger the
   node and the higher the degree value indicated more importance. In our
   study, compounds N-capric acid isopropyl ester (degree = 74),
   (E)-octadec-11-enoic acid (degree = 27), 2H-pyran-2,4(3H)-dione, and
   dihydro-6-methyl-(degree = 9) had larger nodes, indicating that they
   may play more important roles in the anti-AD process.

FIGURE 3.

   [140]FIGURE 3
   [141]Open in a new tab

   Active compounds-intersecting targets network. Blue: active compound
   nodes; green: targets of AD.

3.5 PPI results

   In this part of analysis, 218 intersection targets were imported into
   the STRING database. The exported data was in TSV format. After hiding
   the unrelated nodes, the data were imported into the Cytoscape software
   to bulid the PPI network. The visualized PPI results are shown in
   [142]Figure 4. The PPI network contained 217 nodes and 853 edges, and
   the average degree of nodes was 13.433. In [143]Figure 4A, the closer
   to the center of the concentric circles, the larger the nodes, and the
   darker the color. It also indicated that the closer to the center of
   the concentric circles, the more important the nodes. Taking the degree
   as the screening condition, the higher the degree was the more
   important of the targets in anti-AD process, as shown in [144]Figures
   4B, C. In the figure, epidermal growth factor receptor (EGFR), vascular
   endothelial growth factor A (VEGFA), mitogen-activated protein kinase 3
   (MAPK3), signal transducer and activator of transcription 3 (STAT3) and
   prostaglandin-endoperoxide synthase 2 (PTGS2) were ranked high in
   degree value, indicating that they may play an important role in the
   process of anti-AD.

FIGURE 4.

   [145]FIGURE 4
   [146]Open in a new tab

   (A) PPI network. (B) The subnetwork of key targets. (C) Bar plot of key
   targets.

3.6 GO and KEGG enrichment pathway analysis

   In GO analysis, 288 GO enrichment entries were obtained, including 191
   BP entries, 41 CC entries, 56 MF entries. In KEGG analysis, 86 KEGG
   signaling pathways were obtained. The results are shown in [147]Figure
   5. [148]Figure 5A showed the GO analysis. For BP terms, the major
   enriched GO terms were positive regulation of transcription from the
   RNA polymerase II promoter (GO:0045944, count = 30), signal
   transduction (GO:0007165, count = 24), negative regulation of the
   apoptotic process (GO:0043066, count = 20), response to xenobiotic
   stimulus (GO:0009410, count = 19), and inflammatory response
   (GO:0006954, count = 19). The CC terms were cytosol (GO:0005829, count
   = 68), cytoplasm (GO:0005737, count = 67), plasma membrane (GO:0005886,
   count = 66), nucleus (GO:0005634, count = 54), and integral component
   of membrane (GO:0016021, count = 49). In MF, genes were mainly enriched
   in protein binding (GO:0005515, count = 109), identical protein binding
   (GO:0042802, count = 41), ATP binding (GO:0005524, count = 28), enzyme
   binding (GO:0019899, count = 25), and protein homodimerization activity
   (GO:0042803, count = 21). As shown in [149]Figure 6B, the KEGG
   enrichment analysis revealed that the PI3K-Akt signaling pathway
   (hsa04151, count = 20), neuroactive ligand-receptor interaction
   (hsa04080, count = 17) and ras signaling pathway (hsa04014, count =
   15), MAPK signaling pathway (hsa04010, count = 14), and Lipid and
   atherosclerosis (hsa05417, count = 13) were top-ranked, indicating that
   these pathways play a significant role in the anti-AD process.

FIGURE 5.

   [150]FIGURE 5
   [151]Open in a new tab

   GO enrichment and KEGG pathway analyses. (A) Bar plot of GO enrichment
   analyses. The x-axis indicates the “biological process” in the GO. The
   y-axis indicates the enrichment score about these terms. (B) Bar plot
   of KEGG pathway enrichment analyses. The x-axis indicates the counts of
   the targets in each pathway. The y-axis indicates the main pathways.

FIGURE 6.

   [152]FIGURE 6
   [153]Open in a new tab

   Bioinformatics analysis of active compounds targets associated with tau
   and Aβ pathology. (A) Donut plot. Blue: Aβ-related targets; pink:
   tau-related targets; purple: tau and Aβ-related targets. (B) PPI
   network.

3.7 Bioinformatic analysis of intersection targets associated with Aβ and tau
pathology

   To further explore the relationship between the potential targets of
   active compounds and the possible mechanisms of anti-AD, the
   intersection genes were imported into the AlzDate database. The results
   are shown in [154]Figure 6. Among these targets ([155]Figure 6A), eight
   were associated with Aβ, 8 were associated with tau, and 14 were
   associated with both Aβ and Tau. The 30 targets were imported into the
   STRING database to construct the PPI network, as shown in [156]Figure
   6B. A total of 27 nodes and 70 edges were found. After sorting by
   degree, the core targets included MAPK3 (degree = 16), STAT3 (degree =
   14), VEGFA (degree = 13), MMP2 (degree = 9), TLR2 (degree = 8), and
   HMOX1 (degree = 8), indicating the multi-targeting of Synechococcus sp.
   against AD.

3.8 Molecular docking

   To verify the binding ability between key components and key targets,
   molecular docking was performed using AutoDock Vina and the results
   were visualized using the visualization tool. N-capric acid isopropyl
   ester, (E)-octadec-11-enoic acid and EGFR (8A2D), VEGFA (4QAF) and
   MAPK3 (4QTB) molecules were docked as shown in [157]Table 2. Among
   these targets, VEGFA (4QAF) was found to bind to N-capric acid
   isopropyl ester and (E)-octadec-11-enoic acid with scores of −6.3 and
   −7.2 kcal/mol, respectively, indicating that pocket VEGFA (4QAF) binds
   tightly to N-capric acid isopropyl ester, (E)-octadec-11-enoic acid and
   is stabilized by various interacting forces. Specifically, N-capric
   acid isopropyl ester potentially interacts with ARG858, ASP855, and
   PHE856 residues of EGFR (8A2D) through hydrogen bonding ([158]Figure
   7A). Among them, the distance between N-capric acid isopropyl ester and
   ARG858 was 3.0 Å, the distance to ASP855 was 3.2 Å, and the distance to
   PHE856 was 3.1 Å. N-capric acid isopropyl ester also potentially
   interacted with EGFR (8A2D) via van der waals, alkyl, and pi-alkyl. And
   N-capric acid isopropyl ester potentially interacted with VEGFA (4QAF)
   via van der waals, alkyl, and pi-alkyl. N-capric acid isopropyl ester
   binds to MAPK3 (4QTB) through the formation of hydrogen bond on TYP53
   residue ([159]Figure 7A), the distance between N-capric acid isopropyl
   ester and TYP53 is 3.1 Å; and the N-capric acid isopropyl ester
   potentially interacted with MAPK3 (4QTB) via van der waals, alkyl, and
   pi-alkyl. (E)-octadec-11-enoic acid potentially interacted with the
   MET793 residue of EGFR (8A2D) through hydrogen bonding ([160]Figure
   7B). The distances between (E)-octadec-11-enoic acid and MET793 were
   2.9 and 2.2 Å. It also potentially interacted with EGFR (8A2D) via van
   der Waals, carbon-hydrogen bonding, alkyl, and pi-alkyl.
   (E)-octadec-11-enoic acid potentially interacted with VEGFA (4QAF) via
   van der waals, alkyl, and pi-alkyl. (E)-octadec-11-enoic acid bound to
   MAPK3(4QTB) through the formation of a hydrogen bond at the THR85
   residue, which was separated from THR85 by a distance of 2.4 Å; and
   also potentially interacted with MAPK3(4QTB) via van der waals,
   carbon-hydrogen bond, alkyl, and pi-alkyl. These results suggest an
   important binding role between key components and key targets.

TABLE 2.

   Molecular docking parameters of N-capric acid isopropyl ester,
   (E)-octadec-11-enoic acid, and EGFR, VEGFA, and MAPK3.
   Compounds (Ligand) Targets (PDB ID) Bingding energy (kJ/mol) Center
   Size
   N-capric acid isopropyl ester EGFR (8a2d) −5.9 x = −16, y = −4, z = 30
   x = 21, y = 21, z = 21
   VEGFA (4qaf) −6.3 x = 13, y = 63, z = −1 x = 21, y = 21, z = 21
   MAPK3(4qtb) −6.1 x = 54, y = 20, z = 85 x = 21, y = 21, z = 21
   (E)-octadec-11-enoic acid EGFR (8a2d) −6.7 x = −16, y = −4, z = 30 x =
   28, y = 28, z = 28
   VEGFA (4qaf) −7.2 x = 13, y = 63, z = −1 x = 28, y = 28, z = 28
   MAPK3(4qtb) −6.9 x = 54, y = 20, z = 85 x = 28, y = 28, z = 28
   [161]Open in a new tab

FIGURE 7.

   [162]FIGURE 7
   [163]Open in a new tab

   The protein-ligand of the docking simulation. (A) 3D and 2D maps of
   docking N-capric acid isopropyl ester with EGFR, VEGFA, and MAPK3. (B)
   3D and 2D maps E-octadec-11-enoic acid of docking with EGFR, VEGFA and
   MAPK3.

3.9 Molecular dynamics simulation

   The stability and binding of many protein‒ligand complexes have been
   extensively studied using molecular dynamics (MD) simulations. Based on
   of molecular docking results, molecular dynamics simulations were
   performed to further evaluate the binding activity of VEGFA
   (4QAF)-(E)-octadec-11-enoic acid and VEGFA (4QAF)-N-capric acid
   isopropyl ester. In general, root mean square deviation (RMSD) analysis
   plays an important role in measuring the stability of proteins and
   ligands. Low RMSD values indicate a more stable protein-ligand complex.
   As shown in [164]Figure 8, the RMSD values did not fluctuate
   significantly, indicating revealed that VEGFA-(E)-octadec-11-enoic acid
   and VEGFA-N-capric acid isopropyl ester could bind stably to VEGFA.In
   addition, the RMSF score was used to valuate protein flexibility across
   amino acid residues. Low RMSF values indicate decreased flexibility,
   whereas high RMSF values indicate increased flexibility. As shown in
   [165]Figure 9, both VEGFA-(E)-octadec-11-enoic acid and VEGFA-N-capric
   acid isopropyl ester showed lower fluctuations in the protein residues
   represented by root mean square fluctuations. The majorfluctuations
   were observed near residues 51, 206, 342, and 472, indicating greater
   residue flexibility in these regions of the protein. As another
   indicator of stability, the radius of gyration (Rg) was also analyzed.
   The Rg value indicated that these two protein-ligand complexes did not
   fluctuate significantly during the MD simulation.At the end of the
   100 ns simulation, the Rg values of the complexes were 28.5007Å
   ([166]Figure 10A) and 28.1664Å ([167]Figure 10B), respectively. In
   addition, we further explored the H-bond analysis calculated by
   accompanying molecular dynamics (MD) simulations. Consistent with the
   molecular docking results, the molecular dynamics simulation results
   also indicated that there were no hydrogen bonding interactions between
   the ligand and the target proteins. As shown in the SASA graph
   ([168]Figure 11), the SASA values of VEGFA-(E)-octadec-11-enoic acid
   complexes are relatively stable, while the SASA values of
   VEGFA-N-capric acid isopropyl ester complexes show a slight downward
   trend from 0 to 100 ns.

FIGURE 8.

   [169]FIGURE 8
   [170]Open in a new tab

   MD simulates RMSD trace values of protein-ligand complexes. VEGFA and
   (A) E-octadec-11-enoic acid, (B) N-capric acid isopropyl ester.

FIGURE 9.

   [171]FIGURE 9
   [172]Open in a new tab

   MD simulates RMSF trace values of protein-ligand complexes. VEGFA and
   (A) E-octadec-11-enoic acid. (B) N-capric acid isopropyl ester.

FIGURE 10.

   [173]FIGURE 10
   [174]Open in a new tab

   Radius of gyration (Rg) of protein-ligand complexes. VEGFA and (A)
   E-octadec-11-enoic acid, (B) N-capric acid isopropyl ester.

FIGURE 11.

   [175]FIGURE 11
   [176]Open in a new tab

   Solvent-accessible surface area (SASA) of protein-ligand complexes.
   VEGFA and (A) E-octadec-11-enoic acid, (B) N-capric acid isopropyl
   ester.

4 Discussion

   Many studies have demonstrated that the incidence of AD significantly
   increases with age ([177]Dubois et al., 2021). With the exacerbation of
   the global aging trend, the number of AD patients is growing rapidly,
   and the demand for drugs to defeat AD is becoming increasingly urgent
   ([178]Jeremic et al., 2021). For many years, marine products and their
   derivatives have been recognized as a rich source of therapeutic agents
   and structural diversity ([179]Wolfender et al., 2019). Marine
   Synechococcus is an important genus of oceanic picocyanobacteria
   ([180]Dore et al., 2022). They are widely distributed in the ocean,
   from the equator to the poles, showing an flexible adaptability to
   diverse environment ([181]Callieri, 2017). Marine Synechococcus has
   many advantages such as easy culture and a high growth rate. Recent
   studies have shown that Synechococcus contain various active
   components, such as proteins, pigments, and fatty acids ([182]Liu et
   al., 2021; [183]Kothari et al., 2023). In particular, unsaturated fatty
   acids have been proven to have antioxidant, anti-inflammatory and
   anti-AD-related activities. However, so far, there have been few
   studies on the application of these components or their combinations in
   AD treatment. Consequently, network pharmacology, molecular docking,
   and molecular dynamics simulations were used to investigate the
   potential targets of Synechococcus sp. XM-24 for the treatment of AD.

   Firstly, GC-MS was applied to identify the 22 constituents in the
   Synechococcus sp. XM-24. Among them, that (E)-octadec-11-enoic acid is
   the major constituent, while other volatile compounds such as
   indolizine, isoquinoline, alkene, and alkane, were also been founded.
   The current literature demonstrates that some compounds could have
   potential toxicity. Thus, by using multiple databases, we further
   selected out 10 compounds with higher safety for the further analysis.
   Subsequently, by constructing a network diagram of active
   compound-target-signaling pathways, we found that N-capric acid
   isopropyl ester and (E)-octadec-11-enoic acid had much higher degree
   values in the network analysis than other compounds. Structurally,
   these two compounds are medium-chain fatty acid esters and unsaturated
   fatty acids, respectively. Compound N-capric acid isopropyl ester was
   reported being found in uraria picta Desv., a terrestrial plant
   ([184]Mohan et al., 2020). However, its possible medicinal application
   was not exploring. Moreover, (E)-octadec-11-enoic acid are not commonly
   found in terrestrial photoautotroph. To date, very few existing
   literatures reports the anti-AD activity of these two compounds. There
   are some clues to suggest that these compounds are also promising
   therapeutic candidates to combat AD. Molecular docking results showed
   that both N-capric acid isopropyl ester and (E)-octadec-11-enoic acid
   had certain binding with EGFR, VEGFA, and MAPK3. At the current stage,
   research on medium-chain fatty acid esters is limited, and they are
   often used as food additives ([185]Mayser, 2015). However, recent
   studies have also found that this type of compound has activities such
   as anti-psychotic and anti-bacterial activity ([186]Koch et al., 2022).
   Trans-11-octadecenoic acid is a monounsaturated fatty acid that is also
   present in nuts and sea buckthorn fruit ([187]Arruda et al., 2022).
   Pharmacological studies have shown that trans-11 vaccenic acid appears
   to have no health risks related to industrially produced trans fatty
   acids ([188]Wang X. et al., 2016). In contrast, trans fatty acids are
   beneficial in the prevention of heart disease, obesity, and cancer
   ([189]Lim et al., 2014; [190]Wang et al., 2016). In addition, its
   derivatives have been found to have anti-oxidative stress activity. In
   vitro cell experiments showed that 10-Oxo-trans-11-octadecenoic acid
   (KetoC) can protect HepG2 cells from hydrogen peroxide-induced
   cytotoxicity. KetoC activates the Nrf2-ARE pathway to enhance in vitro
   and in vivo cellular antioxidant responses, further indicating that
   KetoC can prevent various diseases caused by oxidative stress
   ([191]Furumoto et al., 2016). Based on existing studies, there is
   evidence of complex interactions between oxidative stress and the
   pathogenesis of Alzheimer’s disease (AD). In AD patients, elevated
   levels of protein oxidation, lipid peroxidation, and DNA damage
   products have been detected, suggesting the presence of oxidative
   damage ([192]Ionescu-Tucker and Cotman, 2021; [193]Afzal et al., 2021).
   Therefore, the active compounds found in Synechococcus sp. XM-24 hold
   potential in exerting anti-AD activity through the inhibition of
   oxidative stress.

   Based on the intersection targets analysis, we found that these 10
   active compounds mainly play anti-AD effects by acting on 128 core
   targets. Among them, EGFR, VEGFA, MAPK3, STAT3, and PTGS2 have higher
   degree values in the network analysis and have been considered the most
   likely targets to exert anti-AD effects. Moreover, numerous studies
   have shown that the accumulation of Aβ peptide, which are the main
   component of amyloid plaques, is the main factor in the pathogenesis of
   AD ([194]Janus et al., 2000; [195]Tanzi et al., 2004; [196]Guo T. et
   al., 2020a). In addition, there is a close relationship between the
   occurrence of tau protein and AD ([197]Mandelkow and Mandelkow, 1998;
   [198]Kolarova et al., 2012). Therefore, we also analyzed the
   pathological correlation between these targets to Aβ and tau by using
   the Alzdatabase. The results showed that 30 targets among these 128
   targets were significantly correlated with tau, Aβ, or both. Among
   them, MAPK3, STAT3, VEGFA, MMP2, and TLR2 had the highest degree
   values. Furthermore, STAT3, VEGFA, and MAPK3 were also the key targets
   in the PPI network. Prior studies have shown that the transcription
   factor Stat3 is a typical inducer of astrocyte proliferation and can be
   activated in AD model mice and human AD patients ([199]Pekny et al.,
   2016; [200]Escartin et al., 2021). In vitro and in vivo studies have
   demonstrated that the accumulation of tau can cause the inactivation of
   STAT3, which in turn inhibits the expression of N-methyl-d-aspartate
   receptors (NMDAR), and subsequently caused memory impairment ([201]Wan
   et al., 2021). In addition, many recent studies have exhibited that the
   VEGF family plays a significant role in regulating angiogenesis,
   neurogenesis, and neuron survival ([202]Rosenstein and Krum, 2004;
   [203]Eichmann and Simons, 2012). The decrease in VEGF-A levels in
   cerebrospinal fluid is found to have been associated with the higher
   risk of developing AD and cognitive impairment ([204]Hohman et al.,
   2015). Moreover, higher concentrations of VEGFA in cerebrospinal fluid
   were found to be associated with slower rates of hippocampal atrophy
   and cognitive decline, particularly in AD biomarker-positive adults
   ([205]Sweeney et al., 2015). Furthermore, MAPK pathway is an important
   signaling pathway which closely related to cell proliferation,
   differentiation, and apoptosis ([206]Guo Y. J. et al., 2020b). Through
   activation of the MAPK signaling pathway, could strongly influence the
   pathogenesis of AD, such as neuronal apoptosis β-secretase activity and
   γ-secretase activity ([207]Lee et al., 2008; [208]Kim E. K. and Choi,
   2010). Previous studies have shown that stable AD patients treated with
   acetylcholinesterase inhibitors can further improve cognitive
   impairment when combined with cilostazol ([209]Tai S. Y. et al., 2017).
   Based on this, Oguchi T. et al. confirmed through cell experiments that
   cilostazol can inhibit Aβ-induced SH-SY5Y cell neurotoxicity by
   inhibiting oxidative stress and the MAPK signaling pathway ([210]Oguchi
   et al., 2017). According to the research of Tian Qing et al., the
   traditional Chinese medicine-Uncaria rhynchophylla can exert
   therapeutic effects on AD by acting on several key targets such as
   MAPK3 ([211]Zeng et al., 2021b). Based on multiple clinical practice
   studies conducted in China and Japan, Huang-Lian-Jie-Du decoction
   (HLJDD) has significant neuroprotective effects. By employing network
   pharmacology strategies, Zheng et al. confirmed that HLJDD mainly
   exerts anti-AD effects by acting on multiple potential therapeutic
   targets, such as VEGFA, AKT1, TNF, YP53 and PTGS2 ([212]Zheng R. et
   al., 2023). These studies indicate that VEGFA is strongly associated
   with AD.

   In this study, we also conducted GO analysis and KEGG pathway
   enrichment analysis on potential target genes of active compounds
   against AD. The results indicated that the PI3K-Akt signaling pathway,
   Neuroactive ligand-receptor interaction, and Ras signaling pathway were
   the most significantly enriched pathways. Many studies have proved that
   the PI3K-Akt pathway can affect various major biological processes
   including cell proliferation, growth, and survival ([213]Xu et al.,
   2020; [214]Long et al., 2021). These pathophysiological processes are
   closely related to the occurrence of AD ([215]Kumar and Bansal, 2022).
   Hence, the regulation of PI3K-Akt signaling may be helpful for the
   treatment of AD ([216]Fakhri et al., 2021; [217]Kumar and Bansal, 2022;
   [218]Khezri et al., 2023). Fuhai Li et al. proposed that PI3K-Akt is a
   core neuroinflammatory signaling pathway that leads to chronic
   neurodegeneration ([219]Li F. et al., 2021). Ruolan Li et al. used the
   clinical clinically used traditional Chinese medicine Yizhiren (AOF) as
   the research object. In vitro experiments indicated that and
   demonstrated through cell experiments that AOF can significantly
   increase the phosphorylation levels of PI3K and Akt proteins, thereby
   confirming that AOF can exert anti-AD effects by regulating the
   PI3K/Akt pathway ([220]Li R. et al., 2022). The Neuroactive
   ligand-receptor interaction is also considered to be an important
   signaling pathway that associated with AD. According to the analysis of
   AD brain RNA expression chip dataset in the GEO database, the
   downregulation of SIRT3 mRNA in the brain tissue of AD patients may
   promote the progression of AD by affecting this pathway ([221]Song et
   al., 2020). Zhou et al. found multiple potential AD therapeutic targets
   closely related to the neuroactive ligand-receptor interaction
   signaling pathway through the integration of phenotype screening data
   and multi-pharmacology network research ([222]Zhou et al., 2023). In
   addition, according to the literature research, intervening in the Rho
   signaling pathway can effectively regulate the cascade reaction of tau
   and amyloid-beta in AD ([223]Desale et al., 2021; [224]Kumari et al.,
   2023). Plant- and marine-derived secondary metabolites are expected to
   treat multiple neurodegenerative diseases through the Ras/Raf/MAPKs
   pathway, which provides a new perspective for developing promising new
   multi-target anti-AD drugs ([225]Gravandi et al., 2023).

   As a result, Synechococcus sp. XM-24 highly likely execute anti-AD
   effects by regulates PI3K-Akt signaling, Neuroactive ligand-receptor
   interaction and Rho signaling pathway, etc., whereas its active
   compounds of by interacting with STAT3, VEGFA, and MAPK3. Additionally,
   we employed molecular docking and molecular dynamics simulations to
   further investigate the stability and interactions between key
   compounds and core target proteins. Favorable docking scores
   demonstrated a strong and efficient binding between these key compounds
   and core target proteins. Furthermore, during the 70ns MD simulation
   process, the conformation of the complex remained relatively unchanged,
   indicating a stable binding between the key active compounds and core
   target proteins. These results provide strong evidence for the
   potential therapeutic effects of key active compounds in Synechococcus
   sp. XM-24 against Alzheimer’s disease.

   Moreover, it is also worth mentioning that marine cyanobacteria has
   advantages different from terrestrial organisms, such as rapid growth
   rate and easy availability ([226]Rodolfi et al., 2009;
   [227]Bhalamurugan et al., 2018). There, the application of
   bio-renewable compounds in cyanobacteria can be a valuable future
   research direction. Besides, since the growth of cyanobacteria may be
   affected by many factors, including temperature, nutrients, and
   accompanying bacteria and so on. By revealing the key anti-AD compounds
   of Synechococcus sp. XM-24, we can develop an ideal culture condition
   for better outcomes. In summary, in this study, we efficiently selected
   the most promising and drug-like small molecules from Synechococcus sp.
   XM-24 by using GC‒MS technology and network pharmacology research
   strategies. Then, the molecular virtual docking and molecular dynamic
   simulation results were confirmed that these active compounds and their
   combinations are expected to exert anti-AD effects by acting on key
   targets such as STAT3, VEGFA, and MAPK. In general, our study provides
   new research strategies and ideas for the study of Synechococcus in the
   field of anti-AD. However, in future studies, the anti-AD activity of
   these ingredients and their combinations in vitro and in vivo remains
   to be experimentally determined to confirm our hypothesis.

5 Conclusion

   In this study, GC‒MS, network pharmacology analysis, and molecular
   docking were used to identify the active compounds, core targets, and
   signaling pathways of Synechococcus sp. XM-24 to explore its potential
   mechanisms in the treatment of AD.

   A total of 22 active compounds were identified by GC-MS and 10 of them
   were further analyzed using network pharmacology analysis. The result
   show that compounds N-capric acid isopropyl ester and
   (E)-octadec-11-enoic acid obtained higher degrees in the network
   pharmacology analysis, indicating their higher potential in anti-AD
   process. Moreover, molecular docking showed favorable intermolecular
   hydrogen-bond formation between them and three core targets (EGFR,
   VEGFA, and MAPK3). It is expected that these results could provide a
   theoretical framework for the application of marine cyanobacteria in
   the treatment of AD. However, we are of the opinion that further
   studies are necessary in order to elucidate the specific targets and
   mechanisms behind them. Marine cyanobacteria was seems to be clearly an
   essential source of bioactive compounds and will continue to be a
   source of significant drug leads in the pharmaceutical industry.
   Furthermore, the application of advanced technology, e.g., multi-omics
   approach and nanotechnology, will provide us strong supporting on
   discovering novel active compounds and therapeutic method.

Acknowledgments