The target genes were determined from the STRING-db database and the potential targets related to compounds in EOs and compounds in mouse plasma after exposure to EOs were identified ba
BACKGROUND
Background of Salvia rosmarinus Spenn
In the plant classification system, Salvia rosmarinus had the following classification position [1]:
1.1.2 Description and plant part used
Rosemary (S rosmarinus) is a dense, evergreen, hardy, perennial aromatic herb of 90-200 cm in height The leaves were sessile, tough, linear, or linear- lanceolate, 1-4 cm long and 2-4 mm wide, with recurved edges The upper surface is dark green, glabrous, and grainy, while the lower is greyish-green and densely tomentose with a prominent midrib The branches were rigid with fissured bark and the stems were square, woody, and brown Pale blue flowers appeared in cymose inflorescence [2,3] The entire Rosemary plant is used, with the leaves being the primary source of essential oil extraction [4]
Figure 1.1 Rosemary (Salvia rosmarinus Spenn.): (A) whole plant [1], (B) leaves and flowers [1]
In the world, Rosemary was native to Albania, Algeria, Baleares, Corse, Cyprus, East Aegean Is., Egypt, France, Greece, Italy, Libya, Morocco, Portugal, Sardegna, Sicilia, Spain, Tunisia, Turkey, Yugoslavia and introduced into Azores, Bermuda, Bulgaria, Canary Is., Cape Verde, Germany, Great Britain, Ireland, Kriti, Krym, Madeira, Mexico Central, Mexico Southwest, Texas, Trinidad-Tobago, Vietnam [1] In Vietnam, Rosemary was planted a lot in Lam Dong, Da Lat [4]
Figure 1.2 Distribution of S rosmarinus displayed in the world [1]
Renaissance herbals recommended Rosemary for diverse purposes: as a digestif and carminative, for wound healing, for respiratory disorders, to enhance memory, and for others [5] Current European phytotherapy still uses it as a circulatory stimulant, especially for those with low blood pressure; to improve memory and concentration due to increased blood flow to the head Some known traditional and current uses of Rosemary also include: as a restorative for long-term stress and chronic illness; to stimulate adrenal glands; to improve debilities caused by poor circulation and digestion; and to relieve rheumatic muscles when applied as a lotion or diluted essential oil [6]
Ethnopharmacological surveys showed the therapeutic uses of Rosemary among traditional communities in different world regions [7], they were summarized in Table 1.1
Table 1.1 Ethnopharmacology of S rosmarinus in the world
1.1.5 The chemical compositions of S rosmarinus essential oil
The essential oil of S rosmarinus was a colorless or pale-yellow liquid with an intense and spicy aroma Essential oil represents about 1–2.5% of the plant’s total weight and its chemical composition, like other essential oils, diverges according to the geographical area where the plant was collected, climate, part of the plant used, and the extraction method [8-12]
Some characteristic chemical components of this oil include 1,8-cineole, α- pinene, camphor, bornyl acetate, borneol, camphene, α-terpineol, limonene, β-pinene, β-caryophyllene, and myrcene [13]
Essential oil from Morocco and Tunisia often shows a high content of 1,8- cineole, while essential oil from Spain shows a low content of this molecule, and yields a high concentration of camphor and borneol instead; essential oil from France, in turn, has a high concentration of verbenone [14] This data evidenced the chemical composition variation due to the geographical area where the plant is collected
S rosmarinus essential oil (SREO) obtained from leaves showed higher extraction yield during the flowering phase (1.43%) compared to the vegetative phase (1.23%), and plants collected in summer showed almost doubled yield compared to those collected in winter, evidencing chemical composition variation due to plant phenological stage [12]
There is a significant phytochemical variation of SREO according to the used part of the plant Yosr et al (2013) reported that SREO obtained from leaves had 1,8- cineole (35.8%) as the principal component, while caryophyllene (16.7%) was predominant in stem-extracted oil In the oil extracted from flowers, the major
6 compound was caryophyllene oxide (11.9%) [10] However, the leaves are most commonly used for SREO extraction [15]
An example of the influence of the extraction method over the oil composition is SREO extraction by CO2 supercritical fluid had increased concentration of some compounds, such as verbenone (34.16%) and bornyl acetate (17.31%) when compared to SREO extraction by the traditional hydrodistillation method [8,16]
Surveyed phytochemical components found in SREO from 40 different wild plant samples identified 82 molecules and classified them into three categories: monoterpene hydrocarbons, oxygenated monoterpenes, and sesquiterpenes Components that did not fit in either group were classified as “others” In this survey, monoterpene hydrocarbons ranged from 20.9% to 65.6% of sample content; among them, the most abundant molecules were α-pinene (11.8–39.8%) and camphene (3.2– 12.1%); other monoterpene hydrocarbons identified were limonene, β-pinene, terpinolene, and β-myrcene Compounds classified as oxygenated monoterpenes ranged from 27.7% to 74.3% of SREO composition, these compounds were represented majorly by 1,8-cineole (0.1–62.7%) followed by camphor (2.6–30.5%); borneol, linalool, and verbenone were also identified in a significative quantity Lastly, sesquiterpenes ranged from 0.6% to 7.2% of SREO composition and β- caryophyllene was the molecule with the highest quantity; no sesquiterpene alone exceeded 1% The group named “others” represented from 0.2% to 2.2% of the total sample content; no compound of this group exceeded 1%, and octan-3-one was the most representative molecule [16,17]
Until now, about 150 different compounds have been identified in SREO Linear, oxygenated, and bicyclic monoterpenes are the most frequently reported terpenes, such as 1,8-cineole (1), α-pinene (2), camphene (3), β-pinene (4), camphor
(5), borneol (6), bornyl acetate (7), p-cymene (8), limonene (9), α-terpinene (10), β- terpineol (11), terpinene-4-ol (12), verbenone (13), β-myrcene (14), and linalool (15) (Fig 1.3) Many of these monoterpenes are the most representative of SREO, as they are often present, and may represent biochemical markers of this oil [14]
Figure 1.3 The chemical structure of the main compounds was isolated and determined from SREO
1.1.6 Pharmacological activities of S rosmarinus essential oil a) Effects on the nervous system
Anti-depressive and anti-stress effects: In a research conducted by Machado et al (2013), orally administered SREO had antidepressant activity on the tail suspension test (TST) assay by reducing the immobilization time of treated rats compared to a negative control group (vehicle); fluoxetine was used to treat the positive control group This activity was associated with 1,8-cineole, which consisted of 45.1% of the used oil Among the tested doses (ranging from 0.1 to 100 mg/kg), SREO at 100 mg/kg had the highest immobilization-decreasing activity [18] Corticosterone is a hormone associated with stress and is secreted when the
8 hypothalamus-pituitary-adrenal (HPA) axis is activated [19] Chronic elevated serum corticosterone levels may lead to depressive-like behaviors, which increases immobility time in TST [20] The corticosterone levels of mice groups treated with
50 μL/day or 100 μL/day SREO were 33.8 μg/mL and 35.3 μg/mL, respectively, had significantly improved corticosterone levels compared to the control group (44.2 μg/mL) [21]
Memory, learning, and Alzheimerʼs disease effects: Studied the beneficial effects of EO from leaves and flowers of S rosmarinus on scopolamine-induced
Alzheimer-type dementia model in mice The results showed that inhalation of SREO
(4 àL/L or 8 àL/L air) showed a positive result in spontaneous alternation behavior in the Y-maze test, and 1,8 cineole, α-pinene, and β-pinene were found in dominant concentration in the mouse brain [22] Asadi et al have tested the potential of EO from the leaf and aerial part of S rosmarinus on memory in aged and young mice The test group was administered EO (200, 400, 600, and 800 mg/kg i.p.) daily for
7 days, and improvement in memory performances was found in all test groups [23] Filiptsora et al reported the effects of SREO on short-term and numerical memory in a study conducted on 79 school students (aged 13 – 17 years) The result showed positive results as evidenced by increased image and number memory compared to control groups [24] In another study, the aroma of Rosemary oil improved performance in exam students by enhancing free radical scavenging activity and decreasing cortisol levels [25]
Many studies have demonstrated that SREO provides effective antibacterial activity against various microorganisms Using α-pinene-type S rosmarinus oil (50.8% of α-pinene), tested the antimicrobial activity of this oil against several bacteria strains (from genera Salmonella, Shigella, Pseudomonas, Staphylococcus and Escherichia) and fungi (from genera Trichophytonand Aspergillus) For this, they used agar diffusion and agar dilution tests with Gentamicin as a positive control and DMSO as a negative control The zone of inhibition of SREO ranged from 6 mm (Escherichia coli) to 32 mm (Staphylococcus epidermis), and MIC values ranged from < 15.75 mg/mL (Staphylococcus epidermis) to 36.33 mg/mL (Pseudomonas aeruginosa) Overall, their results show moderate antimicrobial activity by SREO, but the values significantly varied according to the strain [26] The essential oil of Rosemary showed antibacterial activity against the four bacteria strains, especially E
9 coli, with an inhibition zone of 18.5 mm, followed by Staphylococcus aureus (14.6 mm), Klebsiella pneumoniae (13.9 mm), and Streptococcus agalactiae (13.1 mm)
Gas Chromatography-Mass Spectroscopy (GC-MS) methods
Gas chromatography (GC) is a separation technique based on the differential partitioning of substances between two immiscible phases, where the mobile phase is a gas (carrier gas) that passes through a stationary phase contained within a column
GC separates compounds or their derivatives that can be vaporized at the analysis temperature The method relies on mechanisms such as adsorption, partition, or size exclusion (using molecular sieves) [34] Gas chromatography-mass spectrometry (GC-MS) is an analytical technique that combines the separation capabilities of GC with the identification power of mass spectrometry to determine various substances in a sample This method can detect trace amounts of a substance, enabling the separation, identification, and quantification of complex chemical mixtures Consequently, GC-MS is ideal for analyzing hundreds of relatively low molecular weight compounds in environmental samples
For over 40 years, mass spectra and chromatographic retention times have been accumulated in publicly available libraries under standardized conditions of 70 eV electron ionization energy, most notably in the NIST 14 Mass Spectral Library collection of the U.S National Institute of Standards and Technology (NIST), but also larger, less well-curated versions (e.g., the Wiley registry), the open-access MassBank database, and the Golm repository [35-38] Similarly, efforts to computationally match mass spectral records to experimental data, and to interpret mass spectra for compound identifications, started in the 1960s and are still ongoing [39-43] The NIST14 library comprises GC-MS mass spectra for 242477 unique compounds of which roughly one-third have recorded standardized retention times, enabling the use
11 of two orthogonal parameters (mass spectral and retention index matching) for compound identification In comparison, LC-MS/MS spectral libraries are significantly smaller in size, with only 8171 unique compounds in the NIST14 library or 12099 unique compounds in the Metlin LC-MS/MS library (which lack retention information) [44].
Molecular docking
Molecular docking is a computational technique used to predict the preferred orientation, affinity, and interactions of a ligand (typically small molecules such as compounds or drugs) within the binding site of a protein or other macromolecules, such as nucleic acids The data on preferred orientation can serve as a basis for estimating the stability and affinity of the interaction between the drug target and the ligand through scoring functions [45]
Molecular docking is a widely used structure-based in silico screening method in drug discovery research This technique enables the identification of novel compounds with potential therapeutic applications, prediction of ligand-target interactions at the molecular level, and determination of structure-activity relationships (SAR) without prior knowledge of the chemical structures of other ligands modulating the target Initially developed to elucidate the mechanisms of molecular recognition between small and large molecules, the use and applications of docking in drug discovery have significantly evolved over the years [46]
Molecular docking is a computational approach that integrates a search algorithm to propose potential ligand binding poses with a scoring function to identify the most likely interaction configuration Given the near-infinite number of possible binding conformations for a ligand on a protein surface, the search algorithm must efficiently and rapidly explore the entire potential interaction space, including conformations proximate to the actual binding site Concurrently, the scoring function must accurately capture the thermodynamic parameters of ligand-protein interactions to distinguish the true binding configuration, ideally corresponding to the global minimum of the scoring function among all proposed conformations Additionally, the scoring function must be computationally efficient to handle large datasets of potential conformations [47]
During physical binding, both the ligand and protein adapt their structures to achieve an optimal fit, a phenomenon known as "induced fit" Consequently, docking
12 algorithms must account for the flexibility of both molecules However, incorporating all degrees of freedom leads to a combinatorial explosion in the conformational space, significantly increasing the complexity of docking To address this, most docking programs perform flexible ligand docking while keeping the target protein rigid Exceptions exist where limited receptor flexibility is introduced, such as through side-chain rotations or by employing an ensemble of experimentally or computationally derived receptor conformations.
Network pharmacology
For decades, the dominant paradigm in drug development has been the design of drugs as specific ligands for a single therapeutic target, aiming to enhance target selectivity and minimize off-target effects However, advances in post-genomic biology have revealed that drug-target interactions are far more complex A study by Yildirim et al demonstrated that the "one drug–one target" model is inadequate, as a single drug can interact with multiple targets, while a single target may be influenced by various drugs [48,49] This complexity is particularly evident in multifactorial diseases such as cancer, diabetes, and hypertension, which often require combination therapies involving drugs with diverse mechanisms of action targeting multiple pathways This concept, polypharmacology, underscores the need for drugs with broad therapeutic effects [50]
Large-scale gene knockout experiments in model organisms have shown that biological systems are robust against these perturbations The consistent phenotypes of these systems often stem from compensatory signaling pathways that overcome the inhibition of individual proteins Network biology theory suggests that altering phenotypes effectively requires simultaneous targeting of multiple critical network components Collectively, the phenotypic resilience observed after gene deletion and network biology principles indicate that, in some cases, highly selective compounds may exhibit reduced therapeutic efficacy Thus, compounds that selectively act on two or more key targets are theoretically more effective than single-target agents [48]
Building on the principles of polypharmacology and network biology, network pharmacology has emerged as a novel paradigm in drug discovery [48] This approach systematically identifies drugs and their therapeutic targets for a given disease by analyzing large datasets, thereby elucidating the mechanisms and pathways underlying drug action in disease treatment
MATERIALS AND METHODS
Essential oils
The essential oils were obtained from Salvia rosmarinus Spenn herb
(Lamiaceae family), harvested in Da Lat City, Lam Dong province, using the steam distillation method The plant specimen was deposited at the Department of Pharmacognosy and Traditional Pharmacy, University of Medicine and Pharmacy, Vietnam National University, Hanoi.
Animals
Male Swiss mice weighing 25 ± 5 g were supplied by the National Institute of Hygiene and Epidemiology The mice were maintained in an animal facility under a controlled environment (23 ± 2°C, 12‐hr light-dark cycle, the light at 7 a.m., free access to food and water, normal diet for at least 1 week before the experiments.
Materials, Instruments, and Software
Solvents: n-hexane (C5H12, 95% purity) and ethanol (C2H6O, 70% purity)
Instruments: Shimadzu Gas chromatograph-Mass spectrometer (Japan), fridge (Japan), high-speed cryogenic centrifuge 16000 rpm (Spain), vortex mixer, micropipette, pipet paster, vial 2 mL, vacutainer tubes EDTA, eppendorf 1,5 mL, scissors, cotton ball
Software and tools: Knime 5.4.3 (https://www.knime.com/downloads),
Smina (https://sourceforge.net/projects/smina/), Google colab (https://colab.google/), Networkx (https://networkx.org), Gprofiler (https://biit.cs.ut.ee/gprofiler/gost), RDKit, RCSB Protein Data Bank (https://www.rcsb.org/), STRING-db (https://string-db.org/), UniProt database (https://www.uniprot.org/).
Methods
2.4.1 Plasma of mouse exposed to essential oils
The mice were divided into two groups including the SREO-exposed and the control groups For the SREO-exposed group, the mouse was exposed to SREO in a jar for 30 minutes Blood was collected from the tail of mice at four times: immediately post-exposure, 30 minutes, 60 minutes, and 90 minutes post-exposure For the control group, blood samples were collected from the tail of mice four times, with a 10-minute interval between each collection The plasma samples were obtained by blood centrifugation at 12000 rpm for 10 minutes in a high-speed cryogenic
14 centrifuge The plasma samples (200 μL) were put into a 1.5 mL clean Eppendorf tube, after that, a 200 μL hexane (1:1 ratio) mixture was pipetted into the sample The sample was shaken for 5 min in a vortex mixer The upper n-hexane layer was separated and anhydrous Na2SO4 was added to remove water Samples were stored at
2.4.2 Gas Chromatography-Mass Spectrometric (GC-MS) analysis
The chemical compositions in essential oil and mice plasma samples were determined using GC-MS The essential oil sample was diluted by n-hexane (1:9 ratio) The samples were filtered through a 0.45 μm membrane before analysis The injection volume was 1 μL, and the split mode was used
Table 2.1 Chromatographic conditions were used in GC-MS analysis
The temperature gradient program is as follows: The initial time is 2 min, and the temperature increase ratio is 3:5 for about 3 min
Each component was identified according to its retention indices and mass spectra Retention indices were calculated using a homologous series of C8-C20 n- alkanes injected under the same experimental conditions Mass spectra were compared with the corresponding standard spectra in the National Institute of Standards and Technology (NIST) library The relative peak area of the components was calculated using the normalization method
2.4.3 Identification of potential compounds and potential gene targets based on molecular docking
Ligand preparation: The three-dimensional (3D) structures of the chemical components derived from Rosemary essential oil were generated using RDKit software The preparation process involved the following steps: (1) addition of hydrogen atoms to the two-dimensional (2D) molecular structures, (2) generation of diverse spatial conformers, (3) application of the MMFF94 force field (kcal/mol), and
(4) optimization of the conformers to minimize free energy, ensuring the selection of the lowest-energy configurations for subsequent analyses
Protein structures preparation: A comprehensive search was conducted to identify proteins associated with central nervous system diseases using the STRING database (https://string-db.org/) The search utilized three key terms: Major Depressive Disorder, Anxiety Disorder, and Cognitive This query retrieved 100 relevant genes, which were subsequently mapped to their corresponding protein names via the UniProt database (https://www.uniprot.org/) After removing duplicates, a curated list of unique target proteins was compiled The 3D structures of these proteins were retrieved from the RCSB Protein Data Bank (https://www.rcsb.org/), prioritizing structures with a high resolution of 2.5 Å and saving them in PDB format To prepare the protein structures for molecular docking, water molecules, and co-crystallized ligands were removed, and hydrogen atoms were added The resulting protein (.pdb) and ligand (.sdf) files were separated to ensure compatibility with the docking software
Molecular docking: Molecular docking was performed using Smina (a fork of AutoDock Vina) to evaluate the binding affinity of the prepared ligands to the active sites of the target proteins The docking process yielded docking scores, representing the free energy of binding (kcal/mol) between each protein-ligand complex Protein-ligand interactions with docking scores ≤ -6 kcal/mol were selected for further investigation The proteins meeting this criterion were converted back to their corresponding gene names using the UniProt database, generating a list of potential gene targets of SREO on Major depressive disorder, Anxiety disorder, and Cognitive
2.4.4 Construction and analysis of the protein-protein interaction (PPI) network
From the potential genes, protein-protein interaction networks were constructed to determine the interaction ability and relationship of these targets by combining STRING-db and Networkx Two protein-protein interaction networks were analyzed using three topological network parameters of the nodes: Degree
Centrality (DC), Closeness Centrality (CC), and Betweenness Centrality (BC)
Higher values of these parameters indicate greater importance of the gene represented by the corresponding node
2.4.5 GO functional and KEGG pathway enrichment analysis
The targets obtained from PPI network analysis were input into the GProfiler to carry out GO enrichment and KEGG pathway enrichment analysis The analysis encompassed GO categories, including Biological Process (BP), Cellular Component
(CC), and Molecular Function (MF), as well as the Kyoto Encyclopedia of Genes and
Genomes (KEGG) biochemical pathways [53] These findings elucidate the molecular mechanisms and biological pathways associated with the potential genes
The obtained results will be analyzed and visualized to elucidate the molecular mechanisms and biological pathways involving candidate genes, thereby revealing their roles and impacts on central nervous system disorders Only GO mechanisms and KEGG pathways with p-values less than 0.05 were retained for further analysis
2.4.6 Construction of the Compound-Target-Pathway Network
The active compound, action targets, and related pathways were imported into
Networkx software to construct a Compound-Target-Pathway network
RESULTS
Chemical compositions of S rosmarinus essential oil and plasma of mouse
The essential oil compositions of the S rosmarinus herb, the plasma compositions of mice exposed to SREO, and the plasma compositions were determined by GC-MS The results would be analyzed for the 30 components with the highest content, calculated by peak area (Fig 3.1 and detailed in Appendix 01)
Figure 3.1 The GC-MS chromatograms of (A) the S rosmarinus essential oil, (C) the exposed SREO, (D) the control plasma samples, and (B) the Venn diagram showed the compounds in essential oil detected (existion) and not detected
(absence) in mouse plasma after exposure to essential oil
Discussion: In SREO, the main compounds were Bicyclo[2.2.1]heptan-2-ol, 4,7,7- trimethyl- (8.97%); Isobornyl formate (8.3%); Isopulegol (7.98%); Bicyclo[3.1.1]hept-3-en-2-one, 4,6,6-trimethyl-, (1S)- (7.34%); and 2(1H)- Naphthalenone, octahydro-, trans- (6.64%) Analysis of mice plasma compositions following 30 minutes of essential oil exposure revealed that 62.2% of the essential oil constituents were detected in the plasma
Target genes and potential target genes
The target genes were determined from the STRING-db database with three key terms Major Depressive Disorder, Anxiety Disorder, and Cognitive, and mapped to their corresponding protein names via the UniProt database The potential targets were identified according to the docking scores ≤ -6 kcal/mol of ligand and protein interaction as well as their corresponding gene names using the UniProt database The results are shown in Table 3.1
Table 3.1 The target genes were determined from the STRING-db database and the potential targets related to compounds in EOs and compounds in mouse plasma after exposure to EOs were identified based on the molecular docking method
Input EOs Sample Plasma EOs Sample Plasma
Discussion: From 46 active ingredients, after using Lipinski’s rule of five to filter, only 45 active ingredients satisfy the medicinal ability These 45 active ingredients were used for docking From 100 genes related to Major Depressive Disorder, Anxiety disorder, and Cognitive from STRING-db corresponding to 5768 proteins from the RPDB database However, only 4922 proteins satisfy the condition of having a resolution of less than 2.5 Å, and some proteins have two co-ligands attached After docking, the proteins with docking values of less than -6 kcal/mol are converted back to potential genes of active essential oils Of 309 UniProt entries, 60 were linked to all diseases, while 158 were associated with only one disease
The mechanism of S.rosmarinus essential oil in the treatment of Major
3.4.1 Construction and analysis of the PPI networks
The PPI networks were constructed from these potential genes, including networks of compounds present in essential oils and networks of essential oil compounds that were detected in the inhaled group of plasma mice The PPI networks were shown in Fig 3.2
Figure 3.2 For Major Depressive Disorder: PPI Network (A) was constructed from potential genes related to compounds in essential oils including 37 nodes and 396 edges PPI Network (B) was constructed from potential genes related to essential oil compounds that were detected in the inhaled group of plasma mice including 36 nodes and 394 edges The colors of the nodes were illustrated in blue, yellow, and red in descending order of degree values
Discussion: To analyze the PPI network from the potential gene related to all compounds in essential oil, this network consisted of 37 nodes and 396 edges Based on the degree values, the top six genes, IL1B (degree = 17), NR3C1 (degree = 17), CRH (degree = 14), NTRK2 (degree = 14), MAOA (degree = 11), and MAPT (degree
= 10) were designated as hub genes The PPI network was constructed from the potential gene related to the essential oil constituents that were detected in the inhaled group of plasma mice This network included 36 genes represented by 36 nodes and
394 edges representing relationships between them Based on the degree values, the top six genes, IL1B (degree = 17), NR3C1 (degree = 17), CRH (degree = 14), NTRK2
(degree = 14), MAOA (degree = 11), and MAPT (degree = 10) were designated as hub genes
3.4.2 GO functional and KEGG pathway enrichment analysis
Potential targets were used for GO functional and KEGG pathway enrichment analysis to explore the mechanism of compounds in essential oils and compounds were detected in the inhaled group of mouse plasma on major depressive disorder The results were presented in Fig 3.3
Figure 3.3 Mechanisms of action of essential oils and existion on Major
Depressive Disorder in the respiratory tract based on GO functional and KEGG pathway analysis The lower the p-value the more significant it is
Discussion: The mechanisms of SREO on Major Depressive Disorder were elucidated through 30 GO:BP, 30 GO:CC, 27 GO:MF, and 30 KEGG pathways The
BP analysis showed that active components of SREO were mainly involved in cell- cell signaling (GO:0007267), response to endogenous (GO:0009719), and regulation of biological quality (GO:0065008) The CC was mainly distributed in the somatodendritic compartment (GO:0036477), neuron projection (GO:0043005), and neuron to neuron synapse (GO:0098984) The MF was mainly related to molecular transducer activity (GO:0060089), signaling receptor activity (GO:0038023), and glutamate receptor activity (GO:0008066) The enrichment analysis of the KEGG pathway showed that 30 enriched categories were identified, these action targets were mainly related to inflammatory bowel disease (KEGG:05321), glutamatergic synapse (KEGG:04724), and amphetamine addiction (KEGG:05031) The mechanisms of existion on Major Depressive Disorder were elucidated through 30 GO:BP, 30 GO:CC, 27 GO:MF, and 30 KEGG pathways The BP analysis showed that active
21 components of existion were mainly involved in cell-cell signaling (GO:0007267), response to endogenous (GO:0009719), and regulation of biological quality (GO:0065008) The CC was mainly distributed in the somatodendritic compartment (GO:0036477), neuron projection (GO:0043005), and neuron to neuron synapse (GO:0098984) The MF was mainly related to molecular transducer activity (GO:0060089), signaling receptor activity (GO:0038023), and glutamate receptor activity (GO:0008066) The enrichment analysis of the KEGG pathway showed that
30 enriched categories were identified, these action targets were mainly related to inflammatory bowel disease (KEGG:05321), glutamatergic synapse (KEGG:04724), and amphetamine addiction (KEGG:05031)
3.4.3 Network construction of Compound-Target-Pathway
The Compound-Target-Pathway network was constructed based on the interactions among bioactive compounds, with potential genes identified and KEGG biochemical pathways analyzed The network provided a visual representation of critical components based on their interactions Active Compound-Target-Pathway were established, respectively Fig 3.4
Figure 3.4 (A) The Compound-Target-Pathway network of essential oil consisted of
133 nodes, with 45 active compounds, 57 potential targets, and 31 biochemical pathways (B) The Compound-Target-Pathway network of existion consisted of 113 nodes, with 28 active compounds, 54 potential targets, and 31 biochemical pathways The blue nodes represented compounds, the gray nodes represented targets, and the yellow nodes represented pathways, each edge represented the interaction between two nodes Compounds with higher interaction counts would be represented by a darker green, indicating greater importance in the network
Discussion: Analysis of the Compound-Target-Pathway network of essential oil revealed that 27 compounds, namely EOs1556 (isobornyl acetate), EOs1035 (bornyl acetate), EOs1818 (neo-isopulegol), EOs2102 (terpinene-4-ol), EOs882 (α- terpineol), EOs13 (bornyl acetate), EOs5 (Linalool), EOs1558 (isobornyl acetate), EOs1201 (citronellol), EOs1910 (p-menth-1-en-8-ol), EOs868 (α-phellandrene), EOs1792 (myrtanol), EOs2175 (trans-myrtanol), EOs1453 (geranyl acetate), EOs94 ((e)-geranyl acetone), EOs989 (β-citronellol), EOs1168 (cis-myrtenol), EOs1616 (isopulegol), EOs1 ((+)-α-terpineol), EOs1328 (endo-bornyl acetate), EOs2100 (terpinen-4-ol), EOs855 (α-fenchene), EOs1663 (linalool), EOs274 (1-α-terpineol), EOs1645 (l-linalool), EOs2105 (terpineol-4), and EOs1167 (cis-myrtanol), were critical nodes, distinguished by their darker green representation Analysis of the Compound-Target-Pathway network of existion revealed that 15 compounds, namely EOs2102 (terpinene-4-ol), EOs2105 (terpineol-4), EOs10((-)-α-terpineol), EOs713 (4-terpineol), EOs2100 (terpinen-4-ol), EOs1 ((+)-α-terpineol), EOs1556 (isobomyl acetate), EOs274 (1-α-terpineol), EOs1663 (linalool), EOs2104 (terpineol), EOs1910 (p-menth-1-en-8-ol), EOs22 ((E)-Geranyl acetone), EOs1035 (bornyl acetate), EOs1645 (l-linalool), and EOs25 ((E)-geranyl acetone), were critical nodes, distinguished by their darker green representation.
The mechanism of S rosmarinus essential oil in the treatment of Anxiety
3.5.1 Construction and analysis of the PPI networks
The PPI networks were constructed from these potential genes, including networks of compounds present in essential oils, and networks of essential oil compounds that were detected in the inhaled group of plasma mice The PPI networks were shown in Fig 3.5
Figure 3.5 For Anxiety Disorder: PPI Network (A) was constructed from potential genes related to compounds in essential oils including 38 nodes and 524 edges PPI Network (B) was constructed from potential genes related to essential oil compounds that were detected in the inhaled group of plasma mice including 38 nodes and 524 edges The colors of the nodes were illustrated in blue, yellow, and red in descending order of degree values
Discussion: To analyze the PPI network from the potential gene related to all compounds in essential oil, this network consisted of 38 nodes and 524 edges Based on the degree values, the top six genes, CRH (degree = 20), NR3C1 (degree = 19), NTRK2 (degree = 19), IL1B (degree = 18), APP (degree = 17), and IFNG (degree 14) were designated as hub genes The PPI network was constructed from the potential gene related to the EO constituents that were detected in the inhaled group of plasma mice This network included 38 genes represented by 38 nodes and 524 edges Based on the degree values, the top six genes, CRH (degree = 20), NR3C1 (degree = 19), NTRK2 (degree = 19), IL1B (degree = 18), APP (degree = 17), and IFNG (degree = 14) were designated as hub genes
3.5.2 GO functional and KEGG pathway enrichment analysis
Potential targets were used for GO functional and KEGG pathway enrichment analysis to explore the mechanism of compounds in essential oils and compounds were detected in the inhaled group of mouse plasma on Anxiety Disorder The results were presented in Fig 3.6
Figure 3.6 Mechanisms of action of essential oils and existion on Anxiety Disorder in the respiratory tract based on GO functional and KEGG pathway analysis The lower the p-value the more significant it is
Discussion: The mechanisms of SREO on Anxiety Disorder were elucidated through
30 GO:BP, 30 GO:CC, 26 GO:MF, and 26 KEGG pathways The BP analysis showed that active components of SREO were mainly involved in cell-cell signaling (GO:0007267), anterograde trans-synaptic signaling (GO:0098916), and chemical synaptic transmission (GO:0007268) The CC was mainly distributed in the somatodendritic compartment (GO:0036477), neuron projection (GO:0043005), and dendrite (GO:0030425) The MF was mainly related to glutamate receptor activity (GO:0008066), amide binding (GO:0033218), and signaling receptor binding (GO:0005102) The enrichment analysis of the KEGG pathway revealed that 26 enriched categories were identified, these action targets were mainly related to cocaine addiction (KEGG:05030), neuroactive ligand-receptor interaction (KEGG:04080), and amphetamine addiction (KEGG:05031) The mechanisms of existion of Anxiety Disorder were elucidated through 30 GO:BP, 30 GO:CC, 26 GO:MF, and 26 KEGG pathways The BP analysis showed that active components of SREO were mainly involved in cell-cell signaling (GO:0007267), anterograde trans- synaptic signaling (GO:0098916), and chemical synaptic transmission (GO:0007268) The CC was mainly distributed in the somatodendritic compartment (GO:0036477), neuron projection (GO:0043005), and dendrite (GO:0030425) The
MF was mainly related to glutamate receptor activity (GO:0008066), amide binding (GO:0033218), and signaling receptor binding (GO:0005102) The enrichment analysis of the KEGG pathway revealed that 26 enriched categories were identified, these action targets were mainly related to cocaine addiction (KEGG:05030),
25 neuroactive ligand-receptor interaction (KEGG:04080), and amphetamine addiction (KEGG:05031)
3.5.3 Network construction of Compound-Target-Pathway
Active Compound-Target-Pathway were established, respectively Fig 3.7
Figure 3.7 (A) The Compound-Target-Pathway network of essential oil consisted of
151 nodes, with 45 active compounds, 80 potential targets, and 26 biochemical pathways (B) The Compound-Target-Pathway network of existion consisted of 132 nodes, with 28 active compounds, 78 potential targets, and 26 biochemical pathways The blue nodes represented compounds, the gray nodes represented targets, and the yellow nodes represented pathways, each edge represented the interaction between two nodes Compounds with higher interaction counts would be represented by a darker green, indicating greater importance in the network
Discussion: Analysis of the Compound-Target-Pathway network of essential oil revealed that 23 compounds, namely EOs1328 (endo-bornyl acetate), EOs2229 (verbenone), EOs1828 (neryl acetate), EOs1616 (isopulegol), EOs1201 (citronellol), EOs2175 (trans-myrtanol), EOs989 (β-citronellol), EOs1848 (o-cymene), EOs882 (α-terpineol), EOs713 (4-terpineol), EOs1696 (melaleucol), EOs1168 (cis-myrtenol), EOs18 ((-)-linalool), EOs1037 (bornyl formate), EOs1792 (myrtanol), EOs5 ((+)- linalool), EOs1453 (geranyl acetate), EOs94 ((e)-geranyl acetone), EOs1 ((+)-α- terpineol), EOs1645 (l-linalool), EOs2104 (terpineol), EOs1738 (methyl eugenol), and EOs1910 (p-menth-1-en-8-ol), were critical nodes, distinguished by their darker green representation Analysis of the Compound-Target-Pathway network of existion revealed that 18 compounds, namely EOs1328 (endo-bornyl acetate), EOs1035
(bornyl acetate), EOs13 ((-)-bornyl acetate), EOs1663 (linalool), EOs25 ((E)-geranyl acetone), EOs1454 (geranyl acetone), EOs1645 (l-linalool), EOs5 ((+)-linalool), EOs1825 (nerol acetate), EOs1910 (p-menth-1-en-8-ol), EOs2102 (terpinene-4-ol), EOs2100 (terpinen-4-ol), EOs1556 (isobomyl acetate), EOs1829 (neryl acetone), EOs1696 (melaleucol), EOs22 ((E)-Geranyl acetone), EOs10 ((-)-α-terpineol), and EOs2105 (terpineol-4), were critical nodes, distinguished by their darker green representation.
The mechanism of S rosmarinus essential oil in the treatment of Cognitive 26 1 Construction and analysis of the PPI networks
The PPI networks were constructed from these potential genes, including networks of compounds present in essential oils, and networks of essential oil compounds that were detected in the inhaled group of plasma mice The PPI networks were shown in Fig 3.8
Figure 3.8 For Cognitive: PPI Network (A) was constructed from potential genes related to compounds in essential oils including 41 nodes and 681 edges PPI Network
(B) was constructed from potential genes related to essential oil compounds that were detected in the inhaled group of plasma mice including 40 nodes and 678 edges The colors of the nodes are illustrated in blue, yellow, and red in descending order of degree values
Discussion: To analyze the PPI network from the potential gene related to all compounds in essential oil, this network consisted of 41 nodes and 681 edges Based on the degree values, the top six genes, IL1B (degree = 26), APP (degree = 25),
NTRK2 (degree = 22), ALB (degree = 22), CRH (degree = 15), and MAPT (degree
= 14) were designated as hub genes The PPI network was constructed from the potential gene related to the essential oil constituents that were detected in the inhaled group of plasma mice This network included 40 genes represented by 40 nodes and
678 edges Based on the degree values, the top six genes, IL1B (degree = 26), APP (degree = 25), NTRK2 (degree = 22), ALB (degree = 22), CRH (degree = 15), and MAPT (degree = 14) were designated as hub genes
3.6.2 GO functional and KEGG pathway enrichment analysis
Potential targets were used for GO functional and KEGG pathway enrichment analysis to explore the mechanism of compounds in essential oils and compounds were detected in the inhaled group of mouse plasma on Cognitive The results were presented in Fig 3.9
Figure 3.9 Mechanisms of action of essential oils and existion on Cognitive in the respiratory tract based on GO functional and KEGG pathway analysis The lower the p-value the more significant it is
Discussion: The mechanisms of SREO on Cognitive were elucidated through 30 GO:BP, 30 GO:CC, 30 GO:MF, and 30 KEGG pathways The BP analysis showed that active components of SREO were mainly involved in cell-cell signaling (GO:0007267), modulation of chemical synaptic transmission (GO:0050804), and regulation of trans-synaptic signaling (GO:0099177) The CC was mainly distributed in the somatodendritic compartment (GO:0036477), synapse (GO:0045202), and neuron projection (GO:0043005) The MF was mainly related to amyloid-beta binding (GO:0001540), identical protein binding (GO:0042802), and signaling receptor binding (GO:0005102) The enrichment analysis of the KEGG pathway revealed that 30 enriched categories were identified, these action targets were mainly
28 related to the dopaminergic synapse (KEGG:04728), Alzheimer disease (KEGG:05010), and amphetamine addiction (KEGG:05031) The mechanisms of existion on Cognitive were elucidated through 30 GO:BP, 30 GO:CC, 30 GO:MF, and 30 KEGG pathways The BP analysis showed that active components of existion were mainly involved in cell-cell signaling (GO:0007267), modulation of chemical synaptic transmission (GO:0050804), and regulation of trans-synaptic signaling (GO:0099177) The CC was mainly distributed in the somatodendritic compartment (GO:0036477), synapse (GO:0045202), and neuron projection (GO:0043005) The
MF was mainly related to amyloid-beta binding (GO:0001540), identical protein binding (GO:0042802), and signaling receptor binding (GO:0005102) The enrichment analysis of the KEGG pathway revealed that 30 enriched categories were identified, these action targets were mainly related to the dopaminergic synapse (KEGG:04728), Alzheimer disease (KEGG:05010), and amphetamine addiction (KEGG:05030)
3.6.3 Network construction of Compound-Target-Pathway
Active Compound-Target-Pathway were established, respectively Fig 3.10
Figure 3.10 (A) The Compound-Target-Pathway network of essential oil consisted of 159 nodes, with 45 active compounds, 75 potential targets, and 39 biochemical pathways (B) The Compound-Target-Pathway network of existion consisted of 141 nodes, with 28 active compounds, 74 potential targets, and 39 biochemical pathways The blue nodes represented compounds, the gray nodes represented targets, and the yellow nodes represented pathways, each edge represented the interaction between two nodes Compounds with higher interaction counts would be represented by a darker green, indicating greater importance in the network
Discussion: Analysis of the Compound-Target-Pathway network of essential oil revealed that 26 compounds, namely EOs1328 (endo-bornyl acetate), EOs1910 (p- menth-1-en-8-ol), EOs2175 (trans-myrtanol), EOs10 ((-)-α-terpineol), EOs25 ((E)- geranyl acetone), EOs1829 (neryl acetone), EOs882 (α-terpineol), EOs1167 (cis- myrtanol), EOs1696 (melaleucol), EOs1663 (linalool), EOs1616 (isopulegol), EOs855 (α-fenchene), EOs1035 (bornyl acetate), EOs713 (4-terpineol), EOs22 ((E)-Geranyl acetone), EOs1738 (methyl eugenol), EOs1848 (o-cymene), EOs1792 (myrtanol), EOs868 (α -phellandrene), EOs1847 (o-cumenol), EOs1825 (nerol acetate), EOs1037 (bornyl formate), EOs1201 (citronellol), EOs1112 (chrysanthenone), EOs5 ((+)-linalool), and EOs2229 (verbenone), were critical nodes, distinguished by their darker green representation Analysis of the Compound-Target-Pathway network of existion revealed that 17 compounds, namely EOs1828 (neryl acetate), EOs1328 (endo-bornyl acetate), EOs1 ((+)-α-terpineol), EOs2105 (terpineol-4), EOs10 ((-)-α-terpineol), EOs18 ((-)-linalool), EOs2104 (terpineol), EOs2102 (terpinene-4-ol), EOs2100 (terpinen-4-ol), EOs13 ((-)-bornyl acetate), EOs1035 (bornyl acetate), EOs94 ((e)-geranyl acetone), EOs1645 (l-linalool), EOs1454 (geranyl acetone), EOs5 ((+)-linalool), EOs274 (1-α-terpineol), and EOs713 (4-terpineol), were critical nodes, distinguished by their darker green representation
DISCUSSION
S rosmarinus was a medicinal herb recognized for its diverse therapeutic properties, including analgesic, anti-aging, anti-inflammatory, antibacterial, anticancer, and neuroprotective effects [7] Prior pharmacological studies have demonstrated the efficacy of compounds in S rosmarinus in managing neurological disorders through oral and inhalation administration, addressing conditions such as anxiety, depression, Alzheimer’s disease, epilepsy, Parkinson’s disease, and addiction withdrawal syndrome [54-57] Despite these findings, the molecular mechanisms underlying the therapeutic effects of SREO via inhalation remain poorly understood Specifically, it is unclear which volatile components of SREO are absorbed into the bloodstream following inhalation and which of these compounds contribute to its therapeutic activity To address this gap, the present study integrates network pharmacology and molecular docking to identify the active compounds, potential targets, and mechanisms of action of SREO in treating diseases in the central nervous system
In this study, GC-MS analysis identified 46 compounds with the highest content of SREO Applying Lipinski's rule of five, 45 compounds were selected as potential drug candidates, including 28 compounds that were detected and 17 compounds that were undetected in the plasma of mice exposed to SREO These compounds, along with 100 genes related to Major Depressive Disorder, Anxiety Disorder, and Cognitive, were collected and evaluated for their interaction potential through molecular docking The results revealed potential compounds and target genes for constructing the PPI network, performing the GO function analysis, and identifying the KEGG pathways, the Compound-Target-Pathway network Consequently, key compounds exhibiting the pharmacological effects of essential oils on the central nervous system were determined
For Major Depressive Disorder, the mechanisms of SREO were elucidated through 30 GO:BP, 30 GO:CC, 27 GO:MF, and 30 KEGG pathways Notably, the main biological function predicted by GO biological process analysis was cell-cell signaling The function in cell regions through GO cellular component analysis was the somatodendritic compartment The GO molecular function was molecular transducer activity Among the KEGG pathways, inflammatory bowel disease emerged as the most significant The important compounds such as isobornyl acetate, α-terpineol, linalool, β-citronellol, and α-fenchene
For Anxiety Disorder, the mechanisms of SREO were elucidated through 30 GO:BP, 30 GO:CC, 26 GO:MF, and 26 KEGG pathways Notably, the main biological function predicted by GO biological process analysis was cell-cell signaling The function in cell regions through GO cellular component analysis was the somatodendritic compartment The GO molecular function was glutamate receptor activity Among the KEGG pathways, cocaine addiction emerged as the most significant The important compounds such as bornyl acetate, verbenone, α-terpineol, citronellol, and linalool
For Cognitive, the mechanisms of SREO were elucidated through 30 GO:BP,
30 GO:CC, 30 GO:MF, and 30 KEGG pathways Notably, the main biological function predicted by GO biological process analysis was cell-cell signaling The function in cell regions through GO cellular component analysis was the somatodendritic compartment The GO molecular function was amyloid-beta binding Among the KEGG pathways, dopaminergic synapse disease emerged as the most significant The important compounds such as trans-myrtanol, α-terpineol, linalool, geranyl acetone, and citronellol
Figure 4.1 Venn diagram showed the relationship between the PPI network of compounds in essential oil and the PPI network of compounds detected in the plasma of mice exposed to the essential oil
Based on the results analyzed, it was evident that the target compounds in the essential oil and those detected in the plasma of mice exposed to the essential oil were identical (Fig 4.1) This indicated that compounds in SREO could be absorbed into the bloodstream via inhalation and potentially exert effects on the central nervous system Therefore, the mechanism of action of SREO through inhalation was based on these components Among the compounds, Linalool, α-Terpineol, and Citronellol have been extensively investigated and confirmed to exhibit pharmacological effects on the central nervous system Silvia Laura et al (2015) demonstrated that the
32 mechanism of the antidepressant-like effect of Linalool is due to its interaction with the serotonergic pathway through postsynaptic 5-HT1A receptors and noradrenergic via α2 receptors [58] According to Graziela et al (2020), Terpineol inhibited depressive-like behavior induced by lipopolysaccharide (LPS) injection, possibly through modulation of dopamine receptor type 2 (D2R), cannabinoid receptor type 1 (CB1R), and cannabinoid receptor type 2 (CB2R) [59] Studies utilizing seizure models and the maximal electroshock (MES) protocol indicate that Citronellol exerts neuroprotective effects that may be associated with modulatory effects on GABAergic neurotransmission or by attenuating neuronal excitability, mainly through inhibition of voltage-gated Na + channels [60] Interestingly the mechanism of action of some of the most frequently prescribed antidepressant drugs also involves the monoaminergic system
After the research process, the thesis title obtained the following results:
1 Forty-six chemical constituents were identified in the SREO, of which 45 compounds exhibited potential pharmacological activity Among these, 28 compounds were detected in the plasma of mice exposed to the essential oil by GC-
2 PPI network analysis, GO function, and KEGG biochemical pathway analyses revealed that SREO exerts effects on central nervous system disorders, such as anxiety and depression, through functions including cell-cell signaling, somatodendritic compartment, and signaling receptor activity, as well as neural signaling pathways such as amphetamine addiction, dopaminergic synapse, and glutamatergic synapse The proposed mechanism of SREO primarily relies on its components detected in mouse plasma following exposure to the essential oil, including compounds such as α-terpineol, linalool, and citronellol
1 The research results have demonstrated the therapeutic potential of SREO on the central nervous system Given the growing demand for herbal medicines as alternatives to antidepressants and sedatives with significant side effects, SREO should be developed into liquid or emulsion products for inhalation that help improve sleep, promote relaxation, and exert sedative effects
1 Salvia rosmarinus Spenn | Plants of the World Online | Kew Science Plants of the World Online http://powo.science.kew.org/taxon/urn:lsid:ipni.org:names:457138-1
2 K.V Peter, ed Handbook of herbs and spices Volume 2 Boca Raton: Woodhead publishing in food science and technology; 2004
3 Council of Europe European pharmacopoeia 10 th ed Strasbourg: European Directorate for the Quality of Medicines and Healthcare; 2019
4 Viện dược liệu Danh lục cây thuốc Việt Nam NXB Khoa học và kỹ thuật; 2016
5 Pardo-de-Santayana M, Rey M, Heinrich M The historical evolution of the medicinal use of rosemary (Rosmarinus officinalis L.), a Spanish panacea JPP 2006;58:A81-A81
6 Chevallier A Encyclopedia of herbal medicine: 550 herbs and remedies for common ailments Penguin; 2016
7 Duke JA Handbook of medicinal herbs 2 nd ed Boca Raton, FL: CRC Press;
8 Mouahid A, Dufour C, Badens E Supercritical CO2 extraction from endemic Corsican plants; comparison of oil composition and extraction yield with hydrodistillation method Journal of CO2 Utilization 2017;20:263-273 doi:
9 Tawfeeq A, Culham A, Davis F, Reeves M Does fertilizer type and method of application cause significant differences in essential oil yield and composition in rosemary (Rosmarinus officinalis L.)? Industrial Crops and Products
10 Yosr Z, Hnia C, Rim T, Mohamed B Changes in essential oil composition and phenolic fraction in Rosmarinus officinalis L var typicus Batt organs during growth and incidence on the antioxidant activity Industrial Crops and Products 2013;43:412-419 doi: 10.1016/j.indcrop.2012.07.044
11 Napoli EM, Curcuruto G, Ruberto G Screening of the essential oil composition of wild Sicilian rosemary Biochemical Systematics and Ecology
12 Tawfik AA, Read PE, and Cuppett SL Rosmarinus officinalis L (Rosemary):
In Vitro Culture, Regeneration of Plants, and the Level of Essential Oil and Monoterpenoid Constituents In: Nagata T, Lửrz H, Widholm JM, eds
Biotechnology in Agriculture and Forestry Medicinal and Aromatic Plants X
Germany: Springer Berlin Heidelberg; 1998: 349-365 doi: 10.1007/978-3-642- 58833-4_18
13 Chávez-González ML, Rodríguez-Herrera R, Aguilar CN Chapter 11 - Essential Oils: A Natural Alternative to Combat Antibiotics Resistance In: Kon
K, Rai M, eds Antibiotic Resistance Mechanisms and New Antimicrobial Approaches Amsterdam: Academic Press; 2016: 227-237 doi: 10.1016/B978-
14 Borges RS, Ortiz BLS, Pereira ACM, Keita H, Carvalho JCT Rosmarinus officinalis essential oil: A review of its phytochemistry, anti-inflammatory activity, and mechanisms of action involved Journal of Ethnopharmacology
15 Rašković A, Milanović I, Pavlović N, et al Antioxidant activity of rosemary (Rosmarinus officinalis L.) essential oil and its hepatoprotective potential BMC
16 Napoli EM, Curcuruto G, Ruberto G Screening of the essential oil composition of wild Sicilian rosemary Biochemical Systematics and Ecology
17 De Cássia Da Silveira E Sá R, Andrade L, De Sousa DP A Review on Anti- Inflammatory Activity of Monoterpenes Molecules 2013;18(1):1227-1254 doi: 10.3390/molecules18011227
18 Machado DG, Cunha MP, Neis VB, et al Antidepressant-like effects of fractions, essential oil, carnosol and betulinic acid isolated from Rosmarinus officinalis L Food Chemistry 2013;136(2):999-1005 doi: 10.1016/j.foodchem.2012.09.028
19 De Kloet ER, Karst H, Joởls M Corticosteroid hormones in the central stress response: Quick-and-slow Frontiers in Neuroendocrinology 2008;29(2):268-
20 Cryan JF, Mombereau C, Vassout A The tail suspension test as a model for assessing antidepressant activity: Review of pharmacological and genetic studies in mice Neuroscience & Biobehavioral Reviews 2005;29(4-5):571-
21 Villareal MO, Ikeya A, Sasaki K, et al Anti-stress and neuronal cell differentiation induction effects of Rosmarinus officinalis L essential oil BMC
22 Satou Y TH, Mizutani I, Koike K The effect of inhalation of essential oil from
Rosmarinus officinalis on scopolamine-induced Alzheimerʼs type dementia model in mice Flavour Fragr J 2017;33:1-5
23 Arzi A, Zarch MB, Nazari Z, Azemi ME Effect of essential oil of leaf and aerial part of Rosmarinus Officinalis on passive avoidance memory in aged and young mice J Alzheimers Dis Parkinsonism 2017;7:6
24 Filiptsova OV, Gazzavi-Rogozina LV, Timoshyna IA, et al The effect of the essential oils of lavender and rosemary on the human short-term memory
25 McCaffrey R, Thomas DJ, Kinzelman AO The Effects of Lavender and
Rosemary Essential Oils on Test-Taking Anxiety Among Graduate Nursing Students Holistic Nursing Practice 2009;23(2):88-93 doi: 10.1097/HNP.0b013e3181a110aa
26 Mekonnen A, Yitayew B, Tesema A, Taddese S In Vitro Antimicrobial Activity of Essential Oil of Thymus schimperi, Matricaria chamomilla, Eucalyptus globulus, and Rosmarinus officinalis International Journal of Microbiology
27 Bajalan I, Rouzbahani R, Pirbalouti AG, Maggi F Antioxidant and antibacterial activities of the essential oils obtained from seven Iranian populations of
Rosmarinus officinalis Industrial Crops and Products 2017;107:305-311 doi:
28 Viuda‐Martos M, Ruiz Navajas Y, Sánchez Zapata E, et al Antioxidant activity of essential oils of five spice plants widely used in a Mediterranean diet Flavour
29 Rašković A, Milanović I, Pavlović N, et al Antioxidant activity of rosemary (Rosmarinus officinalis L.) essential oil and its hepatoprotective potential BMC
30 Takaki I, Bersani-Amado LE, Vendruscolo A, et al Anti-Inflammatory and Antinociceptive Effects of Rosmarinus officinalis L Essential Oil in
Experimental Animal Models Journal of Medicinal Food 2008;11(4):741-746 doi: 10.1089/jmf.2007.0524
31 Mohamed ME, Younis NS, El-Beltagi HS, Mohafez OM The Synergistic Hepatoprotective Activity of Rosemary Essential Oil and Curcumin: The Role of the MEK/ERK Pathway Molecules 2022;27(24):8910 doi:
32 Alaboudi KA, Aziz IM, Almosa AA, et al In vitro and in silico pharmacological effects of Rosmarinus officinalis leaf methanolic extracts and essential oils Sci
33 Wang W, Li N, Luo M, et al Antibacterial Activity and Anticancer Activity of
Rosmarinus officinalis L Essential Oil Compared to That of Its Main
34 Bộ y tế Dược điển Việt Nam V Tập 2 NXB Y học; 2024
35 Babushok VI, Linstrom PJ, Reed JJ, et al Development of a database of gas chromatographic retention properties of organic compounds Journal of Chromatography A 2007;1157(1-2):414-421 doi: 10.1016/j.chroma.2007.05.044
36 Roessner U, Wagner C, Kopka J, et al Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry The Plant Journal
37 Horai H, Arita M, Kanaya S, et al MassBank: a public repository for sharing mass spectral data for life sciences J Mass Spectrom 2010;45(7):703-714 doi: 10.1002/jms.1777
38 Kopka J, Schauer N, Krueger S, et al GMD@CSB.DB: the Golm Metabolome Database Bioinformatics 2005;21(8):635-1638 doi: 10.1093/bioinformatics/bti236
39 McLafferty FW, Hertel RH, Villwock RD Probability based matching of mass spectra Rapid identification of specific compounds in mixtures Organic Mass
40 Venkataraghavan R, McLafferty FW, Van Lear GE Computer‐aided interpretation of mass spectra Org Mass Spectrom 1969;2(1):1-15 doi:
41 Ma Y, Kind T, Yang D, Leon C, Fiehn O MS2Analyzer: A Software for Small Molecule Substructure Annotations from Accurate Tandem Mass Spectra Anal
42 Fiehn O, Kopka J, Trethewey RN, Willmitzer L Identification of Uncommon Plant Metabolites Based on Calculation of Elemental Compositions Using Gas Chromatography and Quadrupole Mass Spectrometry Anal Chem
43 Kumari S, Stevens D, Kind T, et al Applying In-Silico Retention Index and
Mass Spectra Matching for Identification of Unknown Metabolites in Accurate Mass GC-TOF Mass Spectrometry Anal Chem 2011;83(15):5895-5902 doi:
44 Fiehn O Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling CP Molecular Biology
45 Lengauer T, Rarey M Computational methods for biomolecular docking Curr
46 Pinzi L, Rastelli G Molecular Docking: Shifting Paradigms in Drug Discovery
47 Zoete V, Grosdidier A, Michielin O Docking, virtual high throughput screening and in silico fragment‐based drug design J Cellular Molecular Medi
48 Hopkins AL Network pharmacology Nat Biotechnol 2007;25(10):1110-1111 doi: 10.1038/nbt1007-1110
49 Yıldırım MA, Goh K, Cusick ME, et al Drug-target network Nature Biotechnology 2007;25:1119-1126
50 Paolini GV, Shapland RHB, Van Hoorn WP, et al Global mapping of pharmacological space Nat Biotechnol 2006;24(7):805-815 doi:
51 Wolffenbüttel AN, Zamboni A, Becker G, et al Citrus essential oils inhalation by mice: Behavioral testing, GCMS plasma analysis, corticosterone, and melatonin levels evaluation Phytotherapy Research 2018;32(1):160-169 doi: 10.1002/ptr.5964
52 Li W, Hong B, Li Z, et al GC–MS method for determination and pharmacokinetic study of seven volatile constituents in rat plasma after oral administration of the essential oil of Rhizoma Curcumae Journal of Pharmaceutical and Biomedical Analysis 2018;149:577-585 doi:
53 Szklarczyk D, Gable AL, Nastou KC, et al The STRING database in 2021: customizable protein-protein networks, and functional characterization of user- uploaded gene/measurement sets Nucleic Acids Research 2021;49(D1):D605- D612 doi: 10.1093/nar/gkaa1074
54 Rahbardar MG and Hosseinzadeh H Therapeutic effects of rosemary (Rosmarinus officinalis L.) and its active constituents on nervous system disorders Iranian Journal of Basic Medical Sciences 2020;23(9):1100-1112 doi: 10.22038/ijbms.2020.45269.10541
55 Sasaki K, Ferdousi F, Fukumitsu S, et al Antidepressant and anxiolytic-like activities of Rosmarinus officinalis extract in rodent models: Involvement of oxytocinergic system Biomedicine & Pharmacotherapy 2021;144:112291 doi: 10.1016/j.biopha.2021.112291