A Metabolomics Coupled With Chemometrics Strategy to Filter Combinatorial Discriminatory Quality Markers of Crude and Salt-Fired Eucommiae Cortex

A strategy integrated DNA barcoding with metabolomics for screening distinguishable combinatorial chemical quality marker between Pheretima aspergillum and Pheretima vulgaris Chen

Pheretima is an animal-derived traditional Chinese medicines (TCMs). The chemical quality markers of Pheretima used to distinguish different species are still ambiguous. Under this premise, a strategy integrated DNA barcoding with metabolomics is promoted for identifying Pheretima and screening distinguishable combinatorial chemical quality marker (DCQ-marker) between Pheretima aspergillum (P. aspergillum) and Pheretima vulgaris Chen (P. vulgaris). As a result, adenosine, adenine, L-phenylalanine and uridine are successfully selected as DCQ-markers between P. aspergillum and P. vulgaris. This study provides convenient strategy for quickly screening DCQ-marker between P. aspergillum and P. vulgaris. It will be meaningful for further promoting quality control on Pheretima and providing a reference for the quality evaluation of other animal-derived TCMs.

Phytochemical component profiling and anti-renal fibrosis effects of crude and salt-stir fried Eucommiae Cortex extracts on renal fibrosis rats caused by high-purine diet

A prolonged diet laden with purine-rich foods represents a significant contributor to renal fibrosis (RF). Eucommia ulmoides Oliver, a plant homologous to food and medicinal materials, has long been employed to recover kidney function. This investigation presents a strategy integrating chemistry, biochemistry, and metabolomics to evaluate bioactive components and efficiency mechanism of crude and salt-stir fried Eucommiae Cortex (EC) extracts against RF. Firstly, 155 chemical components were identified in the EC extracts and the contents of 19 and 27 compounds decreased and increased respectively after salt-stir frying. Secondly, various biochemical indicators displayed that salt-stir fried EC (SEC) extracts had the optimal anti-RF effects in adenine-induced RF model rats, which were associated with the attenuation of TGF-β signaling pathway. Finally, untargeted metabolomics analysis demonstrated that after treatments with EC and SEC extracts, 30 and 32 efficacy biomarkers were significantly restored in the RF + EC and RF + SEC groups respectively, involving five metabolic pathways.

Leveraging AI technology for distinguishing Eucommiae Cortex processing levels and evaluating anti-fatigue potential

Eucommiae Cortex (ECO) is a traditional medicinal and edible plant endemic to China, highly prized for its numerous health benefits. It typically undergoes special processing before application. The efficacy of ECO is influenced by processing techniques, necessitating the assurance of stability and consistency in its effects. However, existing methods for identifying ECO are cumbersome, thus, there is an urgent need to develop an accurate, rapid, and non-invasive assessment method. Deep learning techniques employing ResNet and Vision Transformer (ViT) models were employed to classify ECO images at various processing levels. Concurrently, the anti-fatigue properties of ECO were assessed through swimming time, pole climbing experiments, and biochemical analyses including SDH, LDH, ATP content, Na+-K+-ATPase, and Ca2+-Mg2+-ATPase indices. We demonstrated the efficacy of using image analysis to automatically classify ECO with a high degree of accuracy. The results indicated that the Vision Transformer model performed exceptionally well, achieving an accuracy rate exceeding 95 % in grading ECO images. Additionally, our study revealed that mice treated with moderately processed ECO displayed enhanced fatigue mitigation compared to other processing levels, as evidenced by multiple assessments.

Quality Evaluation of Kidney Tea Granules From Different Origins Based on TLC, HPLC Fingerprinting, and Quantitative Analysis Combined With Chemical Pattern Recognition

INTRODUCTION

Kidney tea is an essential herbal medicine. It is widely used to treat conditions such as urinary stones, gallstones, and rheumatoid arthritis. There is currently no standardized or widely accepted research strategy for evaluating the quality of kidney tea granules (KTGs) after granulation.

OBJECTIVES

In this study, we aim to establish a comprehensive strategy for evaluating the quality of KTGs produced from different sources of kidney tea.

METHODS

A TLC combined with HPLC method was established to identify the chemical components in KTGs, and HPLC method was used to determine the contents of rosmarinic acid of KTGs. In order to distinguish samples and identify differential components, similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted.

RESULTS

TLC and HPLC detection confirmed three chemical components of KTGs, which are rosmarinic acid, caffeic acid, and lithospermic acid. HPLC fingerprint analysis revealed a total of seven common peaks in 15 batches of KTGs. Similarity analysis showed that the similarity of all 15 batches of KTGs was greater than 0.969. The peak areas of the seven common peaks were identified by chemical pattern recognition, and the results showed that most of the KTGs from different origins were clustered together, with small differences between them. The PCA and OPLS-DA results showed that two principal components can represent 82.638% of the common peaks of KTGs, among which peak 5 represents rosmarinic acid, which is the main differential biomarker of KTGs from different regions. Quantitative analysis of rosmarinic acid in KTG samples was performed using HPLC fingerprint conditions and the content of rosmarinic acid in 15 batches of KTGs samples was measured to be between 8.01-14.61 mg/g.

CONCLUSION

This study combines TLC, HPLC, and chemometrics to establish a stable and reliable method that can quickly and effectively identify the components of KTGs, accurately quantify known components in KTGs, and provide reference for the quality evaluation of KTGs.

Qualitative identification and quantitative comparison of Physochlainae Radix from different regions based on chemometric methods

Physochlainae Radix (PR) is an essential herbal medicine that has been generally applied for treating cough and asthma. In this study, a comprehensive strategy for quality evaluation of PR from different origins was established by integrating qualitative identification, quantitative analysis, and chemometric methods. A total of 58 chemical components were identified by ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS), and a sensitive and rapid UHPLC-QqQ-MS/MS method was established for the simultaneous determination of 12 compounds. In addition, multivariate statistical analysis was applied for discriminant analysis to compare the differences among 30 batches of PR samples. The results showed that the 30 batches of PR collected from four provinces could be clustered into three categories, in which scoparone, protocatechuic acid, tropic acid, and scopolin were important components to distinguish the primary and non-primary producing areas, as well as superior and inferior products of PR. Chemometric results were consistent and validated each other, and systematically explained the intrinsic quality characteristics of PR. This study first demonstrated that LC-MS combined with multivariate statistical analysis, provided a comprehensive and effective means for quality evaluation of PR.

Integrated network pharmacology, transcriptomics and metabolomics analysis to reveal the mechanism of salt Eucommia cortex in the treatment of chronic kidney disease mineral bone disorders via the PPARG/AMPK signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

The skeletal complications associated with chronic kidney diseases from stages 3-5 in individuals are called Chronic Kidney Disease-Mineral Bone Disorder (CKD-MBD), which increases the incidence of cardiovascular diseases drastically and affects the quality of life of patients seriously. Eucommia cortex has the effect of tonifying kidneys and strengthening bones, and salt Eucommia cortex is one of the most commonly used traditional Chinese medicines in the clinical treatment of CKD-MBD instead of Eucommia cortex. However, its mechanism still remains unexplored.

AIM OF THE STUDY

The aim of this study was to investigate the effects and mechanisms of salt Eucommia cortex on CKD-MBD by integrating network pharmacology, transcriptomics, and metabolomics.

MATERIALS AND METHODS

The CKD-MBD mice induced by 5/6 nephrectomy and low calcium/high phosphorus diet were treated with salt Eucommia cortex. The renal functions and bone injuries were evaluated by serum biochemical detection, histopathological analyses, and femur Micro-CT examinations. Differentially expressed genes (DEGs) between the control group and model group, model group and high-dose Eucommia cortex group, model group and high-dose salt Eucommia cortex group were analyzed by transcriptomic analysis. The differentially expressed metabolites (DEMs) between the control group and model group, model group and high-dose Eucommia cortex group, model group and high-dose salt Eucommia cortex group were analyzed by metabolomics analysis.The common targets and pathways were obtained by integrating transcriptomics, metabolomics and network pharmacology, which were identified and verified by in vivo experiments.

RESULTS

The negative impacts on the renal functions and bone injuries were alleviated with salt Eucommia cortex treatment effectively. Compared with CKD-MBD model mice, the levels of serum BUN, Ca and urine Upr were significantly decreased in the salt Eucommia cortex group. And the Integrated network pharmacology, transcriptomics and metabolomics analysis revealed that Peroxisome Proliferative Activated Receptor, Gamma (PPARG) was the only common target, mainly involved by AMPK signaling pathways. The activation of PPARG in the kidney tissue was significantly decreased in CKD-MBD mice but increased in the salt Eucommia cortex treatment. The AMPK signaling pathway were verified that AMPK expression levels were decreased in CKD-MBD mice but increased in the salt Eucommia cortex treatment.

CONCLUSIONS

Our study presented that salt Eucommia cortex alleviated the negative impact of CKD-MBD on the renal injury and bone injury of mice induced by 5/6 nephrectomy with the low calcium/high phosphorus diet effectively, which is highly likely achieved through the PPARG/AMPK signaling pathway.

An efficient method for qualitation and quantitation of multi-components of the herbal medicine Qingjin Yiqi Granules

Qingjin Yiqi Granules (QJYQ) is a Traditional Chinese Medicines (TCMs) prescription for the patients with post-COVID-19 condition. It is essential to carry out the quality evaluation of QJYQ. A comprehensive investigation was conducted by establishing deep-learning assisted mass defect filter (deep-learning MDF) mode for qualitative analysis, ultra-high performance liquid chromatography and scheduled multiple reaction monitoring method (UHPLC-sMRM) for precise quantitation to evaluate the quality of QJYQ. Firstly, a deep-learning MDF was used to classify and characterize the whole phytochemical components of QJYQ based on the mass spectrum (MS) data of ultra-high performance liquid chromatography quadrupole time of flight tandem mass spectrometry (UHPLC-Q-TOF/MS). Secondly, the highly sensitive UHPLC-sMRM data-acquisition method was established to quantify the multi-ingredients of QJYQ. Totally, nine major types of phytochemical compounds in QJYQ were intelligently classified and 163 phytochemicals were initially identified. Furthermore, fifty components were rapidly quantified. The comprehensive evaluation strategy established in this study would provide an effective tool for accurately evaluating the quality of QJYQ as a whole.

Exploration of Habitat-Related Chemical Markers for Stephania tetrandra Applying Multiple Chromatographic and Chemometric Analysis

Geo-authentic herbs refer to medicinal materials produced in a specific region with superior quality. Stephania tetrandra S. Moore (S. tetrandra) is cultivated in many provinces of China, including Anhui, Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Guangdong, Hainan, and Taiwan, among which Jiangxi is the geo-authentic origin. To explore habitat-related chemical markers of herbal medicine, an integrated chromatographic technique including gas chromatography-mass spectrometry (GC-MS), ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS) and ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS/MS) combined with chemometric analysis was established. The established methods manifested that they were clearly divided into two groups according to non-authentic origins and geo-authentic origins, suggesting that the metabolites were closely related to their producing areas. A total of 70 volatile compounds and 50 non-volatile compounds were identified in S. tetrandra. Meanwhile, tetrandrine, fangchinoline, isocorydine, magnocurarine, magnoflorine, boldine, and higenamine as chemical markers were accurately quantified and suggested importance in grouping non-authentic origins and geo-authentic origins samples. The discriminatory analysis also indicated well prediction performance with an accuracy of 80%. The results showed that the multiple chromatographic and chemometric analysis technique could be used as an effective approach for discovering the chemical markers of herbal medicine to fulfill the evaluation of overall chemical consistency among samples from different producing areas.

Machine Learning Approaches in Traditional Chinese Medicine: A Systematic Review

Machine learning (ML), as a branch of artificial intelligence, acquires the potential and meaningful rules from the mass of data via diverse algorithms. Owing to all research of traditional Chinese medicine (TCM) belonging to the digitalization of clinical records or experimental works, a massive and complex amount of data has become an inextricable part of the related studies. It is thus not surprising that ML approaches, as novel and efficient tools to mine the useful knowledge from data, have created inroads in a diversity of scopes of TCM over the past decade of years. However, by browsing lots of literature, we find that not all of the ML approaches perform well in the same field. Upon further consideration, we infer that the specificity may inhere between the ML approaches and their applied fields. This systematic review focuses its attention on the four categories of ML approaches and their eight application scopes in TCM. According to the function, ML approaches are classified into four categories, including classification, regression, clustering, and dimensionality reduction, and into 14 models as follows in more detail: support vector machine, least square-support vector machine, logistic regression, partial least squares regression, k-means clustering, hierarchical cluster analysis, artificial neural network, back propagation neural network, convolutional neural network, decision tree, random forest, principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis. The eight common applied fields are divided into two parts: one for TCM, such as the diagnosis of diseases, the determination of syndromes, and the analysis of prescription, and the other for the related researches of Chinese herbal medicine, such as the quality control, the identification of geographic origins, the pharmacodynamic material basis, the medicinal properties, and the pharmacokinetics and pharmacodynamics. Additionally, this paper discusses the function and feature difference among ML approaches when they are applied to the corresponding fields via comparing their principles. The specificity of each approach to its applied fields has also been affirmed, whereby laying a foundation for subsequent studies applying ML approaches to TCM.

Comparison of the chemical profile differences of Aster tataricus between raw and processed products by metabolomics coupled with chemometrics methods

Aster tataricus, a traditional Chinese herb, has been used to treat cough and asthma for many years. Its raw and processed products have different pharmacological effects in clinical applications. To explore the chemical profile differences of components in A. tataricus processed with different methods, metabolomics methods based on ultra-high-performance liquid chromatography coupled with quadrupole time of flight mass spectrometry and gas chromatography-mass spectrometry were developed. Chemometrics strategy was applied to filter and screen the candidate compounds. The accuracy of differential markers was validated by back propagation neural network. The established methods showed that raw A. tataricus, honey-processed A. tataricus, vinegar-processed A. tataricus, and steamed A. tataricus were clearly divided into four groups, suggesting that the components were closely related to the processing methods. A total of 64 nonvolatile and 43 volatile compounds were identified in A. tataricus, and 22 nonvolatile and 12 volatile differential constituents were selected to distinguish the raw and processed A. tataricus. This study demonstrated that the metabolomics methods coupled with chemometrics were a comprehensive strategy to analyze the chemical profile differences and provided a reliable reference for quality evaluation of A. tataricus.

Essential Oils of New Lippia alba Genotypes Analyzed by Flow-Modulated Comprehensive Two-Dimensional Gas Chromatography (GC×GC) and Chemometric Analysis

Lippia alba (Mill.) N. E. Br. (Verbenaceae) is an aromatic shrub whose essential oils have stood out as a promising source for application in several industrial fields. In this study, the essential oils chemical characterization of eight new L. alba genotypes was performed. The selected materials were collected from the Active Germplasm Bank of the Agronomic Institute and the essential oils were extracted by hydrodistillation. Flow-modulated comprehensive two-dimensional gas chromatography coupled to mass spectrometry (GC×GC-MS) was employed for chemical characterization and evaluation of possible co-eluted compounds. In addition, the chemical analyses were submitted to multivariate statistical analyses. From this investigation, 73 metabolites were identified in the essential oils of the genotypes, from which α-pinene, β-myrcene, 1,8-cineole, linalool, neral, geranial, and caryophyllene oxide were the most abundant compounds among the accessions. This is the first report disclosing α-pinene in higher amounts in L. alba (19.69%). In addition, sabinene, trans-verbenol, myrtenol, (E)-caryophyllene, α-guaiene, germacrene D, and α-bulnesene were also found in relevant quantities in some of the genotypes, and myrtenal and myrtenol could be well separated through the second dimension. Such results contributed to the understanding of the chemical composition of those new genotypes, being important to drive a future industrial applicability and studies in genetic breeding.

Research on Quality Markers of Guizhi Fuling Prescription for Endometriosis Treatment Based on Gray Correlation Analysis Strategy

Guizhi Fuling prescription (GFP), a prestigious prescription of traditional Chinese medicine (TCM) recorded in “Jingui Yaolue,” was composed of five Chinese medicines, including Moutan Cortex, Paeoniae Radix Alba, Persicae Semen, Poria Cocos, and Cinnamomi Ramulus. It was used for the treatment of endometriosis, primary dysmenorrhea, and blood stasis for centuries. However, its Quality Markers of treating endometriosis have not been clearly elucidated. In this study, a rapid ultraperformance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS/MS) method was established for Quality Markers investigation on GFP, and a total of 50 potentially bioactive constituents including triterpenoids, paeoniflorin and its derivatives, phenolic acids, and other species were identified based on their retention time, fragmentation pattern, and accurately measured mass value. Furthermore, regularity of recipe composition and gray correlation analysis revealed that all of the characteristic peaks contributed to the treatment of endometriosis. The relative correlation degrees were greater than 0.6. Among them, peaks 1 and 10, which were most closely correlated to the endometriosis, were identified as amygdalin and cinnamic acid. Finally, all of the active ingredients were molecularly docked with proteins associated with endometriosis by Schrodinger method. Among them, amygdalin, cinnamic acid, paeonol, gallic acid, and paeoniflorin had the lower binding energies. It was proposed that these constituents could be directed at Quality Markers for GFP. Thus, the integrated approach describing for revealing Quality Markers of GFP could be expected to provide a method for quality evaluation.