Identifying the quality markers and optimizing the processing of Gastrodiae rhizoma to treat brain diseases

Abstract

Background: Gastrodiae rhizoma (GR) refers to the dried tuber of Gastrodia elata Bl. and has been used for many centuries to treat brain diseases, such as Alzheimer's disease, major depressive disorder, and cerebral ischemia. However, the processing of GR is complex and varied, resulting in unstable clinical treatment effects. The processing protocols significantly affect the active ingredients and curative effects of GR. We can optimize the processing of GR by identifying quality markers to treat brain diseases.

Methods: Fresh tubers of G. elata Bl. were processed under eight different protocols, and their resulting contents of potentially bioactive compounds were compared using liquid chromatography mass spectrometry to screen the potential quality markers of GR through stoichiometric analysis. The potential quality markers of GR targeting Alzheimer's disease, major depressive disorder, and cerebral ischemia were identified by network pharmacology, and the potentially neuroprotective effects of these components were validated through simulated docking to likely protein targets. Finally, a fit degree analysis was carried out using different composition ratios and proportions of the disease component degree value, and the therapeutic effects of different processing methods on Alzheimer's disease, major depressive disorder, and cerebral ischemia were outlined clearly.

Results: We identified 32 potential therapeutic components and screened 13 quality markers in GR, of which five quality markers (galactinol, glucosyringic acid, parishins C and E, and S-(4-hydroxybenzyl)-glutathione) showed efficacy against all three brain diseases. Furthermore, steaming and microwave-drying during processing can optimize the components of these quality markers for treating the three diseases.

Conclusion: Processing protocols significantly affect the therapeutic components of GR and may also impact its effectiveness in treating brain diseases. Accordingly, optimizing the processing methods of GR to correspond to different therapeutic purposes may improve its efficacy against brain diseases.

Keywords: Gastrodia elata BL; fresh-cut processing; metabolomics; network pharmacology; neuroprotection; quality markers.

FIGURE 1

Representative total ion chromatograms of GR after applying the eight processing protocols. SD: Fresh tubers were sliced and dried under the sun. HD: Fresh tubers were sliced and dried with hot air at 60°C. FD: Fresh tubers were sliced and dried by freezing. MD: Fresh tubers were sliced and dried in a microwave. Steam-SD: Fresh tubers were sliced, steamed, and dried under the sun. Steam-HD: Fresh tubers were steamed and dried with hot air at 60°C. Steam-FD: Fresh tubers were steamed and dried by freezing. Steam-MD: Fresh tubers were steamed and dried in a microwave.

FIGURE 2

Clustering of GR preparations according to processing protocols based on the quality markers. Clustering based on (A) principal component analysis and (B) orthogonal partial least-squares discriminant analysis. (C, D) Identification of the quality markers. PC, principal component; VIP pred, predicted value of the variable importance in projection. (D) References are shown in Supplementary Table S1.

FIGURE 3

Comparison of the levels of 13 quality markers in GR after applying the eight processing protocols. (A) Bar plots where bars with different letters differ significantly from each other (p < 0.05). (B) Heatmap showing the clustering of quality markers (rightmost column) and processing protocols (along the bottom of each column). Results are shown for three batches subjected to each protocol (marked as “1”, “2”, and “3”). Different superscript letters indicate significant differences (p < 0.05). The statistical analysis depicted in (A) entails one-way analysis of variance (Supplementary Table S3), and the normal distribution test is shown in Supplementary Table S2.

FIGURE 4

Network pharmacology of the quality markers in GR after processing and their potential protein targets in Alzheimer’s disease. (A) Overlap between proteins potentially targeted by 10 quality markers and those potentially associated with the disease. (B) Enrichment of the potential targets in the gene ontology biological processes (BPs, top), cellular compartments (CCs, middle), and molecular functions (MFs, bottom). (C) Enrichment of the potential targets in the Kyoto Encyclopedia Of Genes And Genomes pathways. (D) Network of interactions among the quality markers and potential protein targets. (E) Quality markers showing the highest degrees of connectedness in the network in (D).

FIGURE 5

Network pharmacology of the quality markers in GR after processing and their potential protein targets in major depressive disorder. (A) Overlap between proteins potentially targeted by 7 quality markers and those potentially associated with the disease. (B) Enrichment of the potential targets in the gene ontology biological processes (BPs, top), cellular compartments (CCs, middle), and molecular functions (MFs, bottom). (C) Enrichment of potential targets in the Kyoto encyclopedia of genes and genomes pathways. (D) Network of interactions among the quality markers and potential protein targets. (E) Quality markers showing the highest degrees of connectedness in the network in (D).

FIGURE 6

Network pharmacology of the quality markers in GR after fresh-cut processing and their potential protein targets in cerebral ischemia. (A) Overlap between proteins potentially targeted by 9 quality markers and those potentially associated with the disease. (B) Enrichment of the potential targets in the gene ontology biological processes (BPs, top), cellular compartments (CCs, middle), and molecular functions (MFs, bottom). (C) Enrichment of potential targets in the Kyoto Encyclopedia Of Genes And Genomes pathways. (D) Network of interactions among the quality markers and potential protein targets. (E) Quality markers showing the highest degrees of connectedness in the network in (D).

FIGURE 7

Network pharmacology of the quality markers in GR after fresh-cut processing and the potential protein targets simultaneously related to Alzheimer’s disease, major depressive disorder, and cerebral ischemia. (A) Overlap between the component targets and potential protein targets related to each of the diseases. (B) Enrichment of the potential targets in the gene ontology biological processes (BPs, top), cellular compartments (CCs, middle), and molecular functions (MFs, bottom). (C) Enrichment of potential targets in the Kyoto Encyclopedia Of Genes And Genomes pathways. (D) Network of interactions among the quality markers and potential protein targets. (E) Quality markers showing the highest degrees of connectedness in the network in (D).

FIGURE 8

Molecular docking simulations of the quality markers in GR after processing and the potential protein targets simultaneously related to Alzheimer’s disease, major depressive disorder, and cerebral ischemia. Molecular dockings of (A–D) gastrodin with AKT1, MAPK8, SRC, and EGFR; (F–I) S-(4-hydroxybenzyl)-glutathione with AKT1, MAPK8, SRC, and EGFR; (K–N) glucosyringic acid with AKT1, MAPK8, SRC, and EGFR; (P–S) parishin C with AKT1, MAPK8, SRC, and EGFR. (E, J, O, T) Binding energy table of the key components and key targets. The simulated complexes are shown as ribbon diagrams, while the herbal components and side chains in the target proteins predicted to interact with them are shown as ball-and-stick models. Amagnified view of the binding site is shown to the right of each complex. The bar plots compare binding energies for the complex of each herbal component with each protein target. *p < 0.05, ****p < 0.001. The statistical analysis whose results are depicted in (E, J, O, T) entails a one-way analysis of variance (Supplementary Table S5), and the normal distribution test is shown in Supplementary Table S4.

FIGURE 9

Analysis of fit degree. In silico predictions of the optimal processing protocols to prepare GR against (A–D) Alzheimer’s disease, (E–H) major depressive disorder, (I–L) cerebral ischemia, or (M–P) all three conditions simultaneously. In each row, the first panel shows the contributions of the individual quality markers to the total degree of the network of interactions among the quality markers and potential protein targets; the second panel shows the results of principal component analysis to detect the clusters of processing protocols; the third panel shows the fitting curve; the fourth panel shows the fitting degree assigned to each protocol.