Plant Omics: Metabolomics and Network Pharmacology of Liquorice, Indian Ayurvedic Medicine Yashtimadhu

Comparative Metabolomics and Network Pharmacology Analysis Reveal Shared Neuroprotective Mechanisms of Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb

Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb., two nootropics, are recognized in Indian Ayurvedic texts. Studies have attempted to understand their action as memory enhancers and neuroprotectants, but many molecular aspects remain unknown. We propose that Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb. share common neuroprotective mechanisms. Mass spectrometry-based untargeted metabolomics and network pharmacology approach were used to identify potential protein targets for the metabolites from each extract. Phytochemical analyses and cell culture validation studies were also used to assess apoptosis and ROS activity using aqueous extracts prepared from both herbal powders. Further, docking studies were also performed using the LibDock protocol. Untargeted metabolomics and network pharmacology approach unveiled 2751 shared metabolites and 3439 and 2928 non-redundant metabolites from Bacopa monnieri and Centella asiatica extracts, respectively, suggesting a potential common neuroprotective mechanism among these extracts. Protein-target prediction highlighted 92.4% similarity among the proteins interacting with metabolites for these extracts. Among them, kinases mapped to MAPK, mTOR, and PI3K-AKT signaling pathways represented a predominant population. Our results highlight a significant similarity in the metabolome of Bacopa monnieri (L.) Wettst and Centella asiatica (L.) Urb., and their potential protein targets may be attributed to their common neuroprotective functions.

Potential of natural products in inflammation: biological activities, structure-activity relationships, and mechanistic targets

A balance between the development and suppression of inflammation can always be found in the body. When this balance is disturbed, a strong inflammatory response can damage the body. It sometimes is necessary to use drugs with a significant anti-inflammatory effect, such as nonsteroidal anti-inflammatory drugs and steroid hormones, to control inflammation in the body. However, the existing anti-inflammatory drugs have many adverse effects, which can be deadly in severe cases, making research into new safer and more effective anti-inflammatory drugs necessary. Currently, numerous types of natural products with anti-inflammatory activity and distinct structural features are available, and these natural products have great potential for the development of novel anti-inflammatory drugs. This review summarizes 260 natural products and their derivatives with anti-inflammatory activities in the last two decades, classified by their active ingredients, and focuses on their structure-activity relationships in anti-inflammation to lay the foundation for subsequent new drug development. We also elucidate the mechanisms and pathways of natural products that exert anti-inflammatory effects via network pharmacology predictions, providing direction for identifying subsequent targets of anti-inflammatory natural products.

Network pharmacology integrated with experimental validation revealed the mechanism of Fengshi Gutong Capsule in the treatment of osteoarthritis

ETHNOPHARMACOLOGICAL RELEVANCE

Fengshi Gutong Capsule (FSGTC) is a commonly used Chinese medicine for the treatment of joint pain caused by osteoarthritis (OA). However, the mechanism of action of FSGTC for OA remains unclear.

AIM OF THE STUDY

This study aimed to explore the alleviating effects and potential mechanisms of action of FSGTC for OA through data mining, network pharmacology, and in vitro experiments.

MATERIALS AND METHODS

High-performance liquid chromatography (HPLC) was performed to establish the fingerprints of FSGTC and detect the components of FSGTC absorbed in the blood. The effects of FSGTC on inflammation, immunity, and liver and kidney functions in patients with OA were evaluated by mining clinical data. The potential targets and pathways of FSGTC were screened using network pharmacology. Subsequently, CCK-8 assay, flow cytometry, western blotting, RT-qPCR, ELISA, and immunofluorescence were performed in IL-1β-stimulated chondrocytes for further validation.

RESULTS

Eighty-seven common peaks and 10 components were identified using the HPLC fingerprints of 12 batches of samples, and the similarity was in the range of 0.973-0.998. Retrospective clinical analysis demonstrated a significant reduction in inflammatory response levels among patients with OA who received FSGTC treatment. Network pharmacology analysis revealed that FSGTC potentially targeted processes related to inflammation, oxidative stress, and apoptosis. The phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT), The nuclear factor-κB (NF-κB), and janus kinase 2 (JAK2)/signal transducer and activator of transcription 3 (STAT3) signaling pathways were predicted to be the main pathways involved in the therapeutic effects of FSGTC in OA. In vitro, FSGTC-containing serum aided the proliferation of chondrocytes stimulated by IL-1β, while concurrently mitigating apoptosis, suppressing the expression of inflammatory cytokines and oxidative molecules, and inhibiting the degradation of the chondrocyte extracellular matrix (ECM).

CONCLUSIONS

FSGTC alleviates the inflammatory response in patients with OA. This therapeutic effect was attributed to its anti-inflammatory and antioxidant properties, and its ability to promote IL-1β-induced chondrocyte proliferation, inhibit apoptosis, and prevent the degradation of extracellular matrix. These favorable results were associated with the inhibition of the PI3K/AKT, NF-κB, and JAK2/STAT3 signaling pathways.

Metabolomics and Network Pharmacology in the Exploration of the Multi-Targeted Therapeutic Approach of Traditional Medicinal Plants

Metabolomic is generally characterized as a comprehensive and the most copious analytical technique for the identification of targeted and untargeted metabolite diversity in a biological system. Recently, it has exponentially been used for phytochemical analysis and variability among plant metabolites, followed by chemometric analysis. Network pharmacology analysis is a computational technique used for the determination of multi-mechanistic and therapeutic evaluation of chemicals via interaction with the genomes involved in targeted or untargeted diseases. In considering the facts, the present review aims to explore the role of metabolomics and network pharmacology in the scientific validation of therapeutic claims as well as to evaluate the multi-targeted therapeutic approach of traditional Indian medicinal plants. The data was collected from different electronic scientific databases such as Google Scholar, Science Direct, ACS publication, PubMed, Springer, etc., using different keywords such as metabolomics, techniques used in metabolomics, chemometric analysis, a bioinformatic tool for drug discovery and development, network pharmacology, methodology and its role in biological evaluation of chemicals, etc. The screened articles were gathered and evaluated by different experts for their exclusion and inclusion in the final draft of the manuscript. The review findings suggest that metabolomics is one of the recent most precious and effective techniques for metabolite identification in the plant matrix. Various chemometric techniques are copiously used for metabolites discrimination analysis hence validating the unique characteristic of herbal medicines and their derived products concerning their authenticity. Network pharmacology remains the only option for the unique and effective analysis of hundreds of chemicals or metabolites via genomic interaction and thus validating the multi-mechanistic and therapeutic approach to explore the pharmacological aspects of herbal medicines for the management of the disease.

Metabolomics analysis highlights Yashtimadhu (Glycyrrhiza glabra L.)-mediated neuroprotection in a rotenone-induced cellular model of Parkinson's disease by restoring the mTORC1-AMPK1 axis in autophagic regulation

Parkinson's disease (PD) is an age-associated progressive neurodegenerative movement disorder, and its management strategies are known to cause complications with prolonged usage. We aimed to explore the neuroprotective mechanism of the Indian traditional medicine Yashtimadhu, prepared from the dried roots of Glycyrrhiza glabra L. (licorice) in the rotenone-induced cellular model of PD. Retinoic acid-differentiated IMR-32 cells were treated with rotenone (PD model) and Yashtimadhu extract. Mass spectrometry-based untargeted and targeted metabolomic profiling was carried out to discover altered metabolites. The untargeted metabolomics analysis highlighted the rotenone-induced dysregulation and Yashtimadhu-mediated restoration of metabolites involved in the metabolism of nucleic acids, amino acids, lipids, and citric acid cycle. Targeted validation of citric acid cycle metabolites showed decreased α-ketoglutarate and succinate with rotenone treatment and rescued by Yashtimadhu co-treatment. The dysregulation of the citric acid cycle by rotenone-induced energetic stress via dysregulation of the mTORC1-AMPK1 axis was prevented by Yashtimadhu. Yashtimadhu co-treatment restored rotenone-induced ATG7-dependent autophagy and eventually caspases-mediated cell death. Our analysis links the metabolic alterations modulating energy stress and autophagy, which underlies the Yashtimadhu-mediated neuroprotection in the rotenone-induced cellular model of PD.

Data on dose-dependent cytotoxicity of rotenone and neuroprotection conferred by Yashtimadhu (Glycyrrhiza glabra L.) in an in vitro Parkinson's disease model

The data described in this article presents the toxicity of rotenone and the neuroprotective effect of Yashtimadhu choorna (powder) in an in vitro Parkinson's disease model [1]. Yashtimadhu choorna is prepared from the roots of Glycyrrhiza glabra L., commonly known as licorice/ liquorice. The effects of rotenone and Yashtimadhu was assessed using cellular and molecular assays such as cell cytotoxicity assay, live-dead cell staining assay, cell cycle analysis, and western blotting. Protein-protein interaction was studied using ANAT plug-in in Cytoscape. Rotenone displayed time and dose-dependent toxicity, as evidenced by cell cytotoxicity assay and live-dead cell staining assay. Yashtimadhu showed no toxicity and prevented rotenone-induced toxicity. Rotenone and Yashtimadhu displayed differential control on the cell cycle. The Protein-interaction network showed the proteins interacting with ERK-1/2 and the pathways regulated by these interactions. The pathways regulated were primarily involved in cellular oxidative stress and apoptosis response. The data described here will enable the extent of cellular toxicity as a result of rotenone treatment and the neuroprotection conferred by Yashtimadhu choorna. This will enable understanding and exploring the effect of traditional and complementary medicine and aiding the identification of molecular targets to confer neuroprotection in Parkinson's disease.

Prevention of MEK-ERK-1/2 hyper-activation underlines the neuroprotective effect of Glycyrrhiza glabra L. (Yashtimadhu) against rotenone-induced cellular and molecular aberrations

ETHNOPHARMACOLOGICAL RELEVANCE

Yashtimadhu choorna (powder) is prepared from the dried root of Glycyrrhiza glabra L., commonly known as licorice. The Indian Ayurvedic system classifies Yashtimadhu as a Medhya Rasayana that can enhance brain function, improves memory, and possess neuroprotective functions, which can be used against neurodegenerative diseases like Parkinson's disease (PD).

AIM OF THE STUDY

We aimed to decipher the neuroprotective effects of G. glabra L., i.e., Yashtimadhu, in a rotenone-induced PD model.

MATERIALS AND METHODS

Retinoic acid-differentiated IMR-32 cells were treated with rotenone (PD model) and Yashtimadhu, and were assessed for cellular toxicity, live-dead staining, cell cycle, oxidative stress, protein abundance, and kinase phosphorylation.

RESULTS

Yashtimadhu conferred protection against rotenone-induced cytotoxicity, countered cell death, reduced expression of pro-apoptotic proteins (cleaved-caspases-9, and 3, cleaved-PARP, BAX, and BAK) and increased anti-apoptotic protein, BCL-2. Rotenone-induced cell cycle re-entry (G2/M transition), was negated by Yashtimadhu and was confirmed with PCNA levels. Yashtimadhu countered rotenone-mediated activation of mitochondrial proteins involved in oxidative stress, cytochrome-C, PDHA1, and HSP60. Inhibition of rotenone-induced ERK-1/2 hyperphosphorylation prevented activation of apoptosis, which was confirmed with MEK-inhibitor, highlighted the action of Yashtimadhu via ERK-1/2 modulation.

CONCLUSIONS

We provide the evidence for neuroprotection conferred by G. glabra L. (Yashtimadhu) and its mechanism via inhibiting MEK-ERK-1/2 hyper-phosphorylation, prevention of mitochondrial stress, and subsequent prevention of apoptosis. The study highlights Yashtimadhu as a promising candidate with neuroprotective effects, the potential of which can be harnessed for identifying novel therapeutic targets.

MS2Compound: A User-Friendly Compound Identification Tool for LC-MS/MS-Based Metabolomics Data

Metabolomics is a leading frontier of systems science and biomedical innovation. However, metabolite identification in mass spectrometry (MS)-based global metabolomics investigations remains a formidable challenge. Moreover, lack of comprehensive spectral databases hinders accurate identification of compounds in global MS-based metabolomics. Creating experiment-derived metabolite spectral libraries tailored to each experiment is labor-intensive. Therefore, predicted spectral libraries could serve as a better alternative. User-friendly tools are much needed, as the currently available metabolomic analysis tools do not offer adequate provision for users to create or choose context-specific databases. Here, we introduce the MS2Compound, a metabolite identification tool, which can be used to generate a custom database of predicted spectra using the Competitive Fragmentation Modeling-ID (CFM-ID) algorithm, and identify metabolites or compounds from the generated database. The database generator can create databases of the model/context/species used in the metabolomics study. The MS2Compound is also powered with mS-score, a scoring function for matching raw fragment spectra to a predicted spectra database. We demonstrated that mS-score is robust in par with dot product and hypergeometric score in identifying metabolites using benchmarking datasets. We evaluated and highlight here the unique features of the MS2Compound by a re-analysis of a publicly available metabolomic dataset (MassIVE id: MSV000086784) for a complex traditional drug formulation called Triphala. In conclusion, we believe that the omics systems science and biomedical research and innovation community in the field of metabolomics will find the MS2Compound as a user-friendly analysis tool of choice to accelerate future metabolomic analyses.