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. 2024 Jul 23;14:17007. doi: 10.1038/s41598-024-66371-9

Metabolomics study to reveal cognitive improvement with treatment of Scrophularia buergeriana

Dahye Yoon 1, Seon Min Oh 2, Hyeon Seon Na 1, Bo-Ram Choi 1, Kwan-Woo Kim 1, Young-Seob Lee 1, Dong-Ryung Lee 3, Dae Young Lee 4,✉
PMCID: PMC11266482  PMID: 39043762

Abstract

Population aging around the world is rapidly progressing; as a result, cognitive decline developing into dementia is becoming a social problem. There is no drug that can cure dementia, and though drugs that alleviate the symptoms of dementia have been developed, they also have side effects. Therefore, we conducted a study on improving cognitive function using natural products that have secured safety. We confirmed the effect of an extract of Scrophularia buergeriana on scopolamine-induced cognitive impairment through mouse behavioral experiments, and we observed metabolic changes in the cortex and hippocampus via brain tissue dissection after the behavioral experiment. Mitigating effects of S. buergeriana on cognitive impairment caused by scopolamine were observed in passive avoidance and Morris water maze tests. A metabolic analysis revealed biomarkers related to the alleviating effect of cognitive impairment. Niacinamide, tyrosine, uridine, and valine in the cortex and GABA, choline, creatine, formate, fumarate, hypoxanthine, leucine, myo-inositol, pyroglutamate, and taurine in the hippocampus were identified as biomarker candidates for recovering cognitive impairment. In addition to behavioral experiments, this metabolomics study using specific regions of the brain may be helpful in understanding the effects of cognitive improvement.

Subject terms: Metabolomics, Disease model, Cognitive ageing, Cognitive neuroscience, Metabolomics, Metabolomics, Biochemistry

Introduction

Amnesia is a symptom of memory loss regarding facts, information, and experiences; it is often called forgetfulness. Causes of amnesia include stroke, inflammatory reactions from viral infections in the brain, oxygen deficiency in the brain, excessive alcohol intake (Korsakov syndrome), brain cancer, Alzheimer's disease, the administration of sedatives such as benzodiazepine, and epilepsy1. In Alzheimer's patients, choline uptake and acetylcholine synthesis were found to be decreased in the hippocampus and cerebral cortex, whereas the expression of acetylcholine esterase, an enzyme that degrades acetylcholine, was reported to be high2. It was confirmed that the activity of choline acetyltransferase, an enzyme that synthesizes acetylcholine, significantly declines in Alzheimer's patients (with the greatest decline found in the hippocampus3) and decreases the number of nicotinic and muscarinic acetylcholine receptors in the brain4. Scopolamine is a non-specific muscarinic anticholinergic drug1 that is known to reduce learning and cognitive abilities by acting with choline-based probes5. It interferes with the long-term potentiation that occurs in the hippocampus, thus causing amnesia6. To date, the United States Food and Drug Administration (FDA)-approved drugs for Alzheimer’s disease include donepezil, rivastigmine, and galantamine (all of which act as inhibitors of acetylcholine esterase), as well as memantine (an N-methyl-d-aspartate receptor antagonist7).

However, these synthetic drugs have many side effects, including vomiting, nausea, and heart abnormalities7. There is a need for new treatment and prevention through alternative drugs using natural products that have proven safety. Studies on the effect of improving cognitive function using natural products such as ginkgo8, green tea9, and dropwort10 have previously been conducted. In this study, we investigated the effects of Scrophularia buergeriana on cognitive function. Previous studies have reported that E-p-methoxycinnamic acid11, E-harpagoside, and 8-O-(E-p-methoxycinnamoyl) harpagide12 in S. buergeriana are effective at improving cognitive function. We tried to discover whether an extract of S. buergeriana, not a just specific compound, is effective in improving cognitive function using behavioral tests; and at the same time, we tried to understand the effects on cognitive function through a metabolomics approach.

Metabolomics is a type of omics studies that refers to comprehensive studies of the end products of metabolic processes. This can be conducted with a post-genomic tool that reflects the results of intrinsic changes occurring along the central dogma within a cell, and the scope of metabolomics also includes changes due to the external environment13. There are various tools, such as nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–mass spectrometry (LC–MS), and gas chromatography–mass spectrometry (GC–MS), used in metabolomics research. In our study, changes in metabolites were analyzed using NMR-based metabolomics. Due to its simple and fast sample preparation and excellent reproducibility, NMR spectroscopy has been widely used in metabolomics studies. Differences between experimental groups can be analyzed through quantitative changes of metabolites and multivariate statistical analysis of the NMR spectra. In this study, in particular, we tried to select potential biomarkers that act as cognitive improvement effects of S. buergeriana through biomarker analysis using quantitative values of metabolites.

Results

Behavioral experiment

The effects of S. buergeriana on cognitive function were investigated through behavioral experiments and metabolomics studies. A positive control group was treated with ginkgo which was previously reported the efficacy on cognitive function14,15. Moreover, ginkgo extract was publicly notified to improve cognitive functional ingredients for Health Functional Foods by the Minister of Food and Drug Safety of the Republic of Korea. Figure 1 shows the scheme of this study. We found no significant differences in body weight in all groups during the behavioral tests (Fig. 2a). In a passive avoidance test, the scopolamine-induced group (G2) and the normal group (G1) did not show significant differences and administered groups with extracts of S. buergeriana (SBExt) and positive controls (G3–G5) did not show significant differences with G2 during the acquisition trial during training. During the retention trial in the passive avoidance test, latency time was significantly shortened in G2 compared to G1 (p < 0.01), and latency time increased in G4 compared to G2 (p = 0.074) (Fig. 2b).

Figure 1.

Figure 1

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Scheme of this study. (a) The extraction of Scrophularia buergeriana, animal administration, and metabolic study. (b) Experimental timeline for behavioral test.

Figure 2.

Figure 2

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The results of the behavioral test: (a) body weights, (b) latencies in the passive avoidance test, (c) latencies in the Morris water maze test, (d) distances in the Morris water maze test, and (e) cross numbers in the probe trial. Data are expressed as mean ± S.E. and were statistically analyzed with Student’s t-test. ++ statistically different from G1 with p < 0.01; *statistically different from G2 with p < 0.05; **statistically different from G2 with p < 0.01.

Latency time and moving distance for finding a platform were measured in a Morris water maze (MWM) (Fig. 2c,d). In the first day of the MWM, latency time and distance significantly increased in G2 compared to G1 (p < 0.01) and significantly decreased in G4 compared to G2 (p < 0.05). In the second, third, and fourth days of the MWM, latency time and distance significantly increased in G2 compared to G1 (p < 0.01) and significantly decreased in G4 compared to G2 (p < 0.01). In the results of G3, in which a low concentration of SBExt was administered, latency time on the fourth day of the MWM significantly decreased compared to G2 (p < 0.05), and distance significantly decreased compared to G2 on the third and fourth days of the MWM (p < 0.05 and p < 0.01, respectively). After the elimination of the platform, the cross number (the number of stay at the platform) was counted (Fig. 2e). The cross number of G2 significantly decreased compared to G1 (p < 0.01), and the cross numbers of G3 and G4 significantly increased compared to G2 (p < 0.01).

Metabolic profiling

After the behavioral test, brain samples were taken from the animals, and the cerebral cortex and hippocampus were removed for metabolic analyses. Aqueous extracts of the cortex and hippocampus were measured with an NMR spectrometer. Figure 3 shows the representative 1H NMR spectra of the cortex and hippocampus extracts. In total, 37 metabolites in the cortex extracts and 33 metabolites in the hippocampus extracts were identified and quantified. The metabolic compositions of the cortex and hippocampus were found to be similar (Supplementary Table S1).

Figure 3.

Figure 3

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Representative 1H nuclear magnetic resonance (NMR) spectra of the cortex (a) and hippocampus (b) extracts. The major metabolites are annotated on the spectra. 1, acetate; 2, alanine; 3, ascorbate; 4, aspartate; 5, choline; 6. citrate; 7, creatine; 8, creatine phosphate; 9, ethanolamine; 10, formate; 11, fumarate; 12, 4-aminobutyrate (GABA); 13, glutamate; 14, glutamine; 15, glutathione; 16, glycerol; 17, glycine; 18, hypoxanthine; 19, inosine; 20, isoleucine; 21, lactate; 22, leucine; 23, methionine; 24, N-acetylaspartate (NAA); 25, niacinamide; 26, O-phosphocholine (PC); 27, O-phosphoethanolamine (PE); 28, phenylalanine; 29, propionate; 30, pyroglutamate; 31, pyruvate; 32, serine; 33, succinate; 34, taurine; 35, tyrosine; 36, uracil; 37, uridine; 38, valine; 39, myo-inositol; 40, sn-glycero-3-phosphocholine (GPC).

However, the patterns of metabolic perturbation were found to be different in the cortex and hippocampus. Metabolic profiles were subjected to biomarker analysis to filter out significant metabolites. The metabolites affected by scopolamine were first identified via a comparison of G1 and G2. Then, the metabolites affected by 300 mg/kg/day of SBExt were filtered by an area under the curve (AUC) value of 0.7 or higher via a comparison of G2 and G4 (Table 1). The metabolites in which changes in concentration induced by scopolamine and 300 mg/kg/day of SBExt showed opposite tendencies were considered to have changed by the alleviating effect of cognitive deficits. As a result, niacinamide, tyrosine, uridine, and valine were found to be potential biomarkers for the recovery of cognitive function in the cortex (Fig. 4a). In the hippocampus, 4-aminobutyrate (GABA), choline, creatine, formate, fumarate, hypoxanthine, leucine, myo-inositol, pyroglutamate, and taurine were found to be potential biomarkers for the recovery of cognitive function (Fig. 4b).

Table 1.

Results of biomarker analysis with the values of the area under the curve (AUC) > 0.7 analyzed with Student’s t-test. *Significantly different to G1 with p-value < 0.05; **p-value < 0.01; ***p-value < 0.001; #Significantly different to G2 with p-value < 0.05; ##p-value < 0.01; ###p-value < 0.001.

Cortex
Comparison of G1 and G2 Comparison of G2 and G4
Metabolites AUC Log2 FC Metabolites AUC Log2 FC
Valine** 0.8750 − 0.1126 Niacinamide## 0.9219 − 0.0641
Niacinamide* 0.7813 0.0969 Valine 0.8281 0.1219
Tyrosine 0.7031 − 0.0564 Uridine# 0.7813 0.1386
Uridine 0.7031 − 0.0945 Tyrosine 0.7188 0.0919
Hippocampus
Comparison of G1 and G2 Comparison of G2 and G4
Metabolites AUC log2 FC Metabolites AUC log2 FC
Taurine*** 0.9844 − 0.1090 Hypoxanthine### 1.0000 − 0.5514
Pyroglutamate* 0.8438 − 0.2228 Taurine### 1.0000 0.1497
GABA 0.8125 0.2784 Leucine### 0.9844 − 0.1406
Choline* 0.8125 0.1921 GABA## 0.9688 − 0.5998
Creatine 0.7813 − 0.0844 Fumarate### 0.9531 − 0.0872
Formate* 0.7813 0.5397 Choline## 0.9375 0.4537
Fumarate 0.7500 0.0280 Pyroglutamate## 0.8750 0.4458
Hypoxanthine 0.7344 0.0691 myo− Inositol 0.8281 0.0534
myo-Inositol 0.7344 − 0.0712 Creatine# 0.8125 0.0400
Leucine 0.7188 0.0290 Formate 0.7031 − 0.4850
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Figure 4.

Figure 4

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Bar plots of potential biomarkers for the recovery of cognitive function in the cortex (a) and hippocampus (b) analyzed with Student’s t-test. *Significantly different to G1 with p-value < 0.05; **p-value < 0.01; ***p-value < 0.001; #Significantly different to G2 with p-value < 0.05; ##p-value < 0.01; ###p-value < 0.001.

Multivariate statistical analysis

To identify the clustering of the groups, the NMR spectra of cortex and hippocampus were statistically analyzed. Principal component analysis (PCA) with Hotelling’s T2 and the DModX test was firstly analyzed to filter the moderate outliers16. Scatter plots of Hotelling’s T2 versus DModX are shown in Fig. 5a,c, and moderate outliers were detected using a 95% confidence interval cutoff and a critical distance (Dcrit) of 0.0514. Two samples of G1 and one sample of G4 in the cortex were excluded in the multivariate statistical analysis with partial least square discriminant analysis (PLS-DA) (Fig. 5b,d). The results of the cortex’s PLS-DA score plot revealed that the scopolamine-induced group was different from the normal group, and the plots of the SBExt and positive control group were distributed on the negative t[1] side. However, G1 and G2 had no significant difference in the range of the t[1] axis in the t[1] positive area and their PLS analysis of two groups (Supplementary Fig. S3a) showed a low Q2 (− 0.087), and the root mean squared error of estimation (RMSEE, 0.133545) in Y prediction was low. We observed no significant difference in the concentrations of SBExt, and SBExt administration showed a similar pattern to the positive control. The hippocampi of the normal and scopolamine groups had similar patterns. Similar to the cortex, these also do not show a large difference in the range of the t[1] axis, so their PLS analysis of two groups (Supplementary Fig. S4a) showed a low Q2 (0.365). The low concentration SBExt group was distributed on the same side of t[1] axis as the scopolamine group. In the hippocampus, there were differences according to the concentration of SBExt. The high concentration SBExt group had a similar pattern to the positive control group. In the PLS comparison of two groups, G3 and G4 showed a difference by showing a Q2 value of 0.566, and RMSEE value was low at 0.104926, confirming the difference between the two groups (Supplementary Fig. S4h). In the comparison of G4 and G5, they showed a low Q2 value of 0.389 (Supplementary Fig. S4j).

Figure 5.

Figure 5

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Scatter plots of Hotelling’s T2 versus DModX from principal component analysis (PCA) of the (a) cortex and (c) hippocampus; score plots of partial least square discriminant analysis (PLS-DA) of (b) cortex (R2X = 0.282, R2Y = 0.259, and Q2 = − 0.044) and (d) hippocampus (R2X = 0.463, R2Y = 0.593, and Q2 = 0.0413).

Discussion

This study was conducted to evaluate the mitigating effect of the administration of SBExt for 4 weeks on the inhibition of learning ability and memory impairment in a scopolamine-induced amnesia mouse model. It has been reported that some compounds contained in S. buergeriana are effective in improving cognitive ability. However, the content of these single compounds in S. buergeriana is not high, and it is not economical to separate the compound and use it as a material for improving cognitive ability. We tried to confirm through animal behavioral experiments and consequent metabolic changes in brain tissue by treating simply extracted S. buergeriana without separation of compounds. The results of the acquisition trial during the passive avoidance test revealed that all groups showed no significant differences. In the scopolamine-induced group (G2), the time of the latency of retention trial significantly decreased, the latency and distance of the MWM increased, and the cross number of the probe trial significantly decreased compared to those of the normal group (G1). This means that amnesia was triggered by scopolamine administration. In the SBExt-administered groups (G3 and G4), the time of latency in the retention trial of the passive avoidance test increased, the latency and distance of the MWM decreased, and the cross number of the probe trial increased compared to those of G2. The SBExt-administered group with a high concentration (G4) showed better efficacy than that of the positive control (G5). It was confirmed that the SBExt ameliorated scopolamine-induced cognitive dysfunction in the behavioral test.

Metabolic perturbations were observed using NMR-based metabolomics in the cortex and hippocampus after introducing scopolamine and treatment with SBExt. NMR spectroscopy was used to confirm the overall metabolites in the tissue extracts with high reproducibility. However, there are some disadvantages of NMR spectroscopy such as low sensitivity and difficulty in overlapping peak analysis. As the number of metabolites that can be analyzed is limited, 37 metabolites were detected in the cortex and 33 metabolites in the hippocampus. Biomarker analysis was performed to understand whether key metabolites responded to the administration of scopolamine and SBExt. Biomarker analysis can be used for disease diagnosis; it compares the metabolite concentrations of the two groups, draws a receiver operator characteristic (ROC) curve through the calculation of the true-positive and false-positive rates through calculation of sensitivity and specificity, and scores prediction ability via an AUC value17. Sensitivity and specificity were calculated at each cutoff value, and the cutoff value that can most ideally distinguish the two groups was checked. A metabolite that has an AUC value over 0.7 is considered a strong predictor18. In this study, key metabolites that predict cognitive dysfunction were selected by identifying metabolites with the AUC values of 0.7 or more in the comparison to the normal and scopolamine-induced groups. In a comparison between the scopolamine-induced group and the SBExt- and scopolamine-administered group, metabolites with an AUC of 0.7 or more were identified, and key metabolites representing the difference were selected. The metabolites which showed opposite trends in comparisons of the fold changes between the normal group and the scopolamine-induced group and between the scopolamine-induced group and the scopolamine- and SBExt-administered group, were designated as potential biomarkers indicating recovery. In the cortex, niacinamide, tyrosine, uridine, and valine were found to be changed after scopolamine induction, and they showed recovery patterns following SBExt administration (Supplementary Fig. S5). In the hippocampus, GABA, choline, creatine, formate, fumarate, hypoxanthine, leucine, myo-inositol, pyroglutamate, and taurine showed recovery patterns of cognitive dysfunction (Supplementary Fig. S6). The cortex and hippocampus showed different metabolic changes. More metabolites were significantly changed in the hippocampus than in the cortex. It was confirmed that most of the changed metabolites were involved in cognitive function or neurotransmission in the brain.

Niacinamide decreased with scopolamine induction in the cortex, but it increased after SBExt treatment. Niacinamide is known to prevent the hyperphosphorylation of the Tau protein19. Microtubules, which are responsible for transmitting information in brain cells, are destabilized by the phosphorylation of the Tau protein20. Therefore, niacinamide plays a role in maintaining the stabilization of microtubules by inhibiting the phosphorylation of the Tau protein such that information can be well-transmitted in the brain (Fig. 6a). Tyrosine and valine increased with scopolamine induction and decreased with SBExt treatment in the cortex. Tyrosine is a precursor to dopamine, a neurotransmitter involved in cognitive function, and is converted to dopamine via L-Dopa (Fig. 6b)21,22. Branched chain amino acids (BCAAs), such as valine, have important effects on the production of glutamate and GABA, which act as neurotransmitters. GABA is an inhibitory neurotransmitter, and the activation of the GABA system increases the release of acetylcholine in the cortex (Fig. 6c)23. Scopolamine is an antagonist of muscarinic receptors that temporarily blocks information transmission by inhibiting the receptor binding of acetylcholine, thus causing cognitive impairment. Acetylcholine is degraded by acetylcholinesterase. It was reported that the activity of acetylcholinesterase increases when scopolamine is administered24; it can lead to the activation of the GABA system by reducing acetylcholine. On the other hand, in the hippocampus, the concentration of GABA was found to decrease with scopolamine induction and increased after SBExt treatment in our study. A previous study showed that the expression rate of a peptide called somatostatin secreted by GABA in the cerebrospinal fluid of Alzheimer’s patients significantly decreased25. In our study, pyroglutamate increased with scopolamine treatment and decreased with SBExt treatment in the hippocampus. Pyroglutamate is a metabolite involved in the release of GABA23. Taurine, which activates the GABA system by activating the GABAA receptor, also showed the same pattern as pyroglutamate in our study (Fig. 6d)26. There are several pathways that work to regulate the action of GABA in the brain, and the results of this study suggest that GABA is regulated by different actions in the cortex and hippocampus. In a previous study, it was confirmed that taurine increases in the hippocampus when there is an impairment of cognitive ability27. Taurine is also considered as a marker of astrocyte activity along with myo-inositol and has been reported to play an important role in brain osmoregulation28. It can be seen that cognitive dysfunction induced by scopolamine also affects astrocyte activity and maintenance of brain cell membranes. Choline related to cell membrane metabolism is another metabolite that is affected by an increase in acetylcholinesterase. Choline is formed by acetylcholine degradation by acetylcholinesterase. In our results, choline was decreased after scopolamine induction. Choline is required not only in the composition of acetylcholine but also for the synthesis of phosphatidylcholine, and it plays a role as methyl donor via betaine29. Consumption seems to be greater than release of choline by decomposition of acetylcholine, and it can directly affect brain function by causing death of brain cells according to choline deficiency30. In our results, it was confirmed that the choline content was reduced in G2 by scopolamine induction and recovered in G4 by SBExt treatment.

Figure 6.

Figure 6

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Metabolic pathways involving candidate biomarkers. (a) Stabilization of microtubule of niacinamide, (b) tyrosine as a precursor to dopamine, (c) branched chain amino acids involved in GABA production, and (d) taurine and pyroglutamate activating GABA system. Figures were created using Microsoft PowerPoint.

In the hippocampus, metabolites involved in energy metabolism in the brain were also found to be changed. Creatine is an energy storage source, and the concentration of creatine was found to increase after scopolamine induction. Like taurine, creatine was reported to increase in the hippocampus of cognitive dysfunction27. Formate is involved in energy metabolism because it participates in mitochondrial oxidative phosphorylation and the formation of adenosine triphosphate (ATP)31, and fumarate is an intermediate of the tricarboxylic acid (TCA) cycle, which is involved in energy metabolism. Both metabolites were found to have decreased after scopolamine induction, but they were found to have increased after SBExt treatment. These results show that energy metabolism in the brain is affected by cognitive impairment. It has been reported that the brain is an organ with large energy demand, and dysfunction of energy metabolism can occur in a region-specific manner32. In the results of this study, different metabolic responses were shown in the cortex and hippocampus according to the same induction of cognitive dysfunction, and in particular, it was confirmed that more metabolites in the hippocampus responded more sensitively than in the cortex. The effect of mitigating cognitive impairment according to the intake of SBExt was confirmed through a metabolic approach, and if future studies are conducted to connect sequential metabolic changes, it will be possible to understand cognitive decline and the mechanism of action to treat it.

Materials and methods

Ethical statement

All animal experimental protocols were reviewed and approved (permission number: 2018-07-008) by the Institutional Animal Care and Use Committee (IACUC) of the Nonclinical Research Institute, ChemOn Inc. (https://www.chemon.co.kr:11044/english/, Yongin-si, Gyeonggi-do, Republic of Korea). All procedures were performed in accordance with the Good Laboratory Practice (GLP) regulation published by the Korean Ministry of Food and Drug Safety (MFDS, 2017)33. This facility used for animal studies has been accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC International). After the behavior tests, mice were anesthetized by 2 to 5% of isoflurane inhalation. This study was carried out in compliance with the ARRIVE guidelines.

Plant materials

The roots of S. buergeriana were cultivated and harvested in National Institute of Horticultural and Herbal Science (NIHHS), Rural Development Administration (RDA), Eumseong, Republic of Korea. They were cultivated according to the protocol of the ‘Scrophularia buergeriana Standard Cultivation Guide’ developed by the RDA, Republic of Korea34. The plant material was identified by Prof. Seung Hwan Yang, Chonnam National University, Yeosu, Republic of Korea. A voucher specimen (MPS006278) was deposited at the Herbarium of the Department of Herbal Crop Research, NIHHS, RDA, Eumseong, Republic of Korea.

Extraction of S. buergeriana

The dried roots of S. buergeriana were extracted with 70% ethanol for 2–4 h at 70–90 °C, filter-processed, concentrated, and dried35. The standardized SBExt was dissolved in 0.5% carboxy-methylcellulose (CMC) and subsequently used for the in vivo study.

Experimental animals

Male C57BL/6N mice (8 weeks old; Orient. Co. Ltd., Gyeonggi, Korea) were used after a one-week adaptation period (20–26 °C; 12 h light cycle from 08:00 to 20:00; food provided by Doo Yeol Biotech; water ad libitum). We monitored changes in body weight, food intake, and water intake once a week. To enhance the well-being of the animals, we provided a sanitary environment to prevent disease and to ensure proper breeding and management.

Scopolamine injection and drug administration

The mice were divided into five groups: a normal control group (G1), a scopolamine-treated group (G2), groups co-treated with scopolamine and SBExt (30 (G3) or 300 (G4) mg/kg/day), and a group co-treated with scopolamine and a ginkgo extract with 50 mg/kg/day as a positive control group (G5). The concentration of SBExt administered was selected based on the results of the SH-SY5Y cell experiment in a previous study36. SBExt and ginkgo extract were dissolved in 0.5% CMC and orally administered for 4 weeks (Day 28). The passive avoidance test and MWM were performed for 3 (Days 15–17) and 7 (Days 22–28) days, respectively. Scopolamine was dissolved in sterile saline (NaCl 0.9% (w/v)). The mice were anesthetized with Zoletil® (Virbac, France) and xylazine (Rompum®, Germany) (4:1, v/v). Scopolamine was injected (1 mg/kg/day, ip) from the 16th to 17th days (passive avoidance test) and from the 24th to 28th days (MWM) of SBExt administration, as well as 30 min after the administration of SBExt. Data were obtained for a total of 40 animals, 8 in each group.

Passive avoidance test

Passive avoidance test equipment (Med, Associates Inc.) was used to perform the passive avoidance experiments. This apparatus was divided into light and dark compartments, with a guillotine door in the middle. The bottom was gridded to administer an electric shock. Experiments were performed at the same time of day for 3 consecutive days at 24 h intervals. For adaptation training, the animals were placed in a shaded area for 2 min and then placed back in an illuminated area. When the animal moved into the shaded area, it was immediately placed in the illuminated compartment (Day 15). Twenty-four hours later (Day 16), two training sessions were performed every 2 min. After 60 s of adaptation, the animals were allowed to freely move between the two compartments for 120 s. However, upon moving to the shaded area, the guillotine door was closed, and a 0.20 mA scrambled shock was applied for 2 s. The animals that failed to move were excluded from the experiment. On the last day of testing (Day 17), the animal was placed in the illuminated area and the guillotine door was opened. The time taken to move to the shaded area was measured.

Morris water maze (MWM) test

The MWM test was performed 22 days after SBExt administration. A platform was placed in one of the four designated release points in the water pool, allowing the animal a search time of 60 s. After the platform was located, the mice were allowed to remain on it for 30 s. If the animal failed to find the platform within 60 s, the animal was placed on the platform and rested for about 30 s. All animals completed two trials per day, with the position of the platform in the water pool randomly changed. Using this method, we measured the time taken to locate the platform by repeating the test for 7 consecutive days (training: 2 days; behavioral test: 4 days; probe trial: 1 day). On the last day (Day 28), a probe trial was conducted. Here, 1 h after vehicle or drug administration, after the platform was removed from the pool, the number of times the animal crossed the platform space was measured for 60 s.

Preparation of tissue samples

After behavioral testing, the mice were anesthetized and sacrificed for biochemical studies. The cortex and hippocampus were separated from the brain tissue and then immediately stored at -80 °C until further assessment.

Extraction of brain samples for NMR spectroscopy

Metabolites in the cortex (100 mg) and hippocampus (10 mg) were extracted in a 1:1 (v/v) acetonitrile/water mixture. After centrifugation, the supernatant was lyophilized overnight. The lyophilized extract was re-dissolved with a 0.2 M sodium phosphate buffer (pH 7.5) made with deuterium oxide. For the calibration of the chemical shift and the quantification of metabolites, 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid sodium salt (TSP-d4) was added to the buffer.

NMR data acquisition and data analysis

Cortex extracts were measured using a 600.17 MHz Agilent NMR spectrometer (Agilent technologies, Santa Clara, CA, USA). One-dimensional (1D) 1H-NMR spectroscopy was performed using a Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence with presaturation for the suppression of macromolecules and water signals. The acquisition time was 2.999 s, the 90° pulse-width (pw90) was 13 μs, and the number of scans was 128. Two-dimensional (2D) 1H-1H correlation spectroscopy (COSY) and 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) data were acquired for metabolite confirmation in the cortex. Hippocampus extracts were measured using a 700.40 MHz Bruker NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) at the KBSI Ochang Center, Republic of Korea. 1D 1H-NMR spectroscopy was conducted using a CPMG (cpmgpr1d) pulse sequence. The acquisition time was 1.802 s, the pw90 was 13.18 μs, and the number of scans was 128. 2D 1H–1H COSY and 1H–13C HSQC data were acquired for metabolite confirmation in the hippocampus. All NMR spectra were manually phased, baseline-corrected, and analyzed for metabolic profiling using Chenomx NMR Suit 8.4 Professional (Chenomx Inc, Canada).

Statistical analysis

The data of behavioral tests are expressed as the means ± standard error of the mean (SEM) and were assessed using the SPSS program (version 22.0, SPSS Inc., Chicago, IL, USA). Different treatment groups were compared using the Student’s t-test using Origin 7.0 software (OriginLab, Northhampton, MA, USA). The differences between mean values were considered statistically significant and highly significant at p < 0.05 and p < 0.01, respectively. For the statistical analysis of metabolic data, the binning of NMR spectra was conducted with Chenomx NMR Suite 8.4 Professional. The binning results were normalized to total area and aligned by the icoshift algorithm of MATLAB (The MathWorks, Natick, MA, USA). Multivariate statistical analyses of NMR spectra were conducted with SIMCA 15.0.2 software (Umetrics, Umeå, Sweden). PCA was conducted to confirm the distribution of unsupervised samples. Therefore, 8 samples for each group were used in further analyses. PLS-DA was performed to visualize group clustering. The biomarker analysis of quantified metabolites was performed with MetaboAnalyst 5.0 (https://www.metaboanalyst.ca).

Supplementary Information

Supplementary Information. (3MB, docx)

Acknowledgements

This work was supported by the “Cooperative Research Program for Agriculture Science and Technology Development” (Project noPJ01601501), Rural Development Administration, Republic of Korea.

Author contributions

D.Y.L. conceived and designed the experiments; S.M.O., H.S.N., and D.-R.L. contributed to the plant material preparation; D.Y and B.-R.C. performed the NMR experiments; D.Y., K.-W.K. and Y.-S.L. analyzed the experimental data; D.Y. and D.Y.L. wrote the paper; and D.Y.L. managed the research project. All authors helped prepare the paper and approved the final version.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Footnotes

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Associated Data

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Supplementary Materials

Supplementary Information. (3MB, docx)

Data Availability Statement

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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