Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map

TLDA: A transfer learning based dual-augmentation strategy for traditional Chinese Medicine syndrome differentiation in rare disease

The Traditional Chinese Medicine (TCM) has demonstrated its significant medical value over the decades, particularly during the COVID-19 pandemic. TCM-AI interdisciplinary models have been proposed to model TCM knowledge, diagnosis, and treatment experiments in clinical practice. Among them, numerous models have been developed to simulate the syndrome differentiation process of human TCM doctors for automatic syndrome diagnosis. However, these models are designed for normal scenarios and trained using a supervised learning paradigm which needs tens of thousands of training samples. They fail to effectively differentiate syndromes in rare disease scenarios where the available TCM electronic medical records (EMRs) are very limited for each unique syndrome. To address the challenge of rare diseases, this study proposes a simple yet effective method called Transfer Learning based Dual-Augmentation (TLDA). TLDA aims to augment the limited EMRs at both the sample-level and feature-level, enriching the pathological and medical information during training. Extended experiments involving 11 comparison models, including the state-of-the-art model, demonstrate the effectiveness of TLDA. TLDA outperforms all comparison models by a significant margin. Furthermore, TLDA can also be extended to other medical tasks when the EMRs for diagnosis are limited in samples.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

High-throughput transcriptional profiling of perturbations by Panax ginseng saponins and Panax notoginseng saponins using TCM-seq

Panax ginseng (PG) and Panax notoginseng (PN) are highly valuable Chinese medicines (CM). Although both CMs have similar active constituents, their clinical applications are clearly different. Over the past decade, RNA sequencing (RNA-seq) analysis has been employed to investigate the molecular mechanisms of extracts or monomers. However, owing to the limited number of samples in standard RNA-seq, few studies have systematically compared the effects of PG and PN spanning multiple conditions at the transcriptomic level. Here, we developed an approach that simultaneously profiles transcriptome changes for multiplexed samples using RNA-seq (TCM-seq), a high-throughput, low-cost workflow to molecularly evaluate CM perturbations. A species-mixing experiment was conducted to illustrate the accuracy of sample multiplexing in TCM-seq. Transcriptomes from repeated samples were used to verify the robustness of TCM-seq. We then focused on the primary active components, Panax notoginseng saponins (PNS) and Panax ginseng saponins (PGS) extracted from PN and PG, respectively. We also characterized the transcriptome changes of 10 cell lines, treated with four different doses of PNS and PGS, using TCM-seq to compare the differences in their perturbing effects on genes, functional pathways, gene modules, and molecular networks. The results of transcriptional data analysis showed that the transcriptional patterns of various cell lines were significantly distinct. PGS exhibited a stronger regulatory effect on genes involved in cardiovascular disease, whereas PNS resulted in a greater coagulation effect on vascular endothelial cells. This study proposes a paradigm to comprehensively explore the differences in mechanisms of action between CMs based on transcriptome readouts.

Integration of the Connectivity Map and Pathway Analysis to Predict Plant Extract’s Medicinal Properties—The Study Case of Sarcopoterium spinosum L

Medicinal properties of plants are usually identified based on knowledge of traditional medicine or using low-throughput screens for specific pharmacological activities. The former is very biased since it requires prior knowledge of plants’ properties, while the latter depends on a specific screening system and will miss medicinal activities not covered by the screen. We sought to enrich our understanding of the biological activities of Sarcopoterium spinosum L. root extract based on transcriptome changes to uncover a plurality of possible pharmacological effects without the need for prior knowledge or functional screening. We integrated Gene Set Enrichment Analysis of the RNAseq data to identify pathways affected by the treatment of cells with the extract and perturbational signatures in the CMAP database to enhance the validity of the results. Activities of signaling pathways were measured using immunoblotting with phospho-specific antibodies. Mitochondrial membrane potential was assessed using JC-1 staining. SARS-CoV-2-induced cell killing was assessed in Vero E6 and A549 cells using an MTT assay. Here, we identified transcriptome changes following exposure of cultured cells to the medicinal plant Sarcopoterium spinosum L. root extract. By integrating algorithms of GSEA and CMAP, we confirmed known anti-cancer activities of the extract and predicted novel biological effects on oxidative phosphorylation and interferon pathways. Experimental validation of these pathways uncovered strong activation of autophagy, including mitophagy, and excellent protection from SARS-CoV-2 infection. Our study shows that gene expression analysis alone is insufficient for predicting biological effects since some of the changes reflect compensatory effects, and additional biochemical tests provide necessary corrections. This study defines the advantages and limitations of transcriptome analysis in predicting the biological and medicinal effects of the Sarcopoterium spinosum L. extract. Such analysis could be used as a general approach for predicting the medicinal properties of plants.

A Novel Based-Network Strategy to Identify Phytochemicals from Radix Salviae Miltiorrhizae (Danshen) for Treating Alzheimer's Disease

Alzheimer’s disease (AD) is a common age-related neurodegenerative disease that strikes millions worldwide. Herein, we demonstrate a new approach based on network target to identify anti-AD compounds from Danshen. Network pharmacology and molecular docking were employed to establish the DS-AD network, which mainly involved apoptosis of neuron cells. Then network scoring was confirmed via Connectivity Map analysis. M308 (Danshenxinkun D) was an anti-AD candidate with a high score (p < 0.01). Furthermore, we conducted ex vivo experiments with H2O2-treated PC12 cells to verify the neuroprotective effect of Salvia miltiorrhiza-containing plasma (SMP), and UPLC-Q-TOF/MS and RT-qPCR were performed to demonstrate the anti-AD activity of M308 from SMP. Results revealed that SMP could enhance cell viability and level of acetylcholine. AO/EB staining and Mitochondrial membrane potential (MMP) analysis showed that SMP significantly suppressed apoptosis, which may be due to anti-oxidative stress activity. Moreover, the effects of M308 and SMP on expressions of PSEN1, DRD2, and APP mRNA were consistent, and M308 can significantly reverse the expression of PSEN1 and DRD2 mRNA in H2O2-treated PC12 cells. The strategy based on the network could be employed to identify anti-AD compounds from Chinese herbs. Notably, M308 stands out as a promising anti-AD candidate for development.

Diterpenoid Vinigrol specifically activates ATF4/DDIT3-mediated PERK arm of unfolded protein response to drive non-apoptotic death of breast cancer cells

Vinigrol is a natural diterpenoid with unprecedented chemical structure, driving great efforts into its total synthesis in the past decades. Despite anti-hypertension and anti-clot ever reported, comprehensive investigations on bioactions and molecular mechanisms of Vinigrol are entirely missing. Here we firstly carried out a complete functional prediction of Vinigrol using a transcriptome-based strategy coupled with multiple bioinformatic analyses and identified "anti-cancer" as the most prominent biofunction ahead of anti-hypertension and anti-depression/psychosis. Broad cytotoxicity was subsequently confirmed on multiple cancer types. Further mechanistic investigation on several breast cancer cells revealed that its anti-cancer effect was mainly through activating PERK/eIF2α arm of unfolded protein response (UPR) and subsequent non-apoptotic cell death independent of caspase activities. The other two branches of UPR, IRE1α and ATF6, were functionally irrelevant to Vinigrol-induced cell death. Using CRISPR/Cas9-based gene activation, repression, and knockout systems, we identified the essential contribution of ATF4 and DDIT3, not ATF6, to the death process. This study unraveled a broad anti-cancer function of Vinigrol and its underlying targets and regulatory mechanisms. It paved the way for further inspection on the structure-efficacy relationship of the whole compound family, making them a novel cluster of PERK-specific stress activators for experimental and clinical uses.

Revealing the Angiopathy of Lacrimal Gland Lesion in Type 2 Diabetes

For a better understanding of diabetic angiopathy (DA), the potential biomarkers in lacrimal DA and its potential mechanism, we evaluated the morphological and hemodynamic alterations of lacrimal glands (LGs) in patients with type 2 diabetes and healthy counterparts by color Doppler flow imaging (CDFI). We further established a type 2 diabetic mice model and performed hematoxylin-eosin (HE) staining, immunofluorescence staining of CD31, RNA-sequencing analysis, and connectivity map (CMap) analysis. We found atrophy and ischemia in patients with type 2 diabetes and mice models. Furthermore, we identified 846 differentially expressed genes (DEGs) between type 2 diabetes mellitus (T2DM) and vehicle mice by RNA-seq. The gene ontology (GO) analysis indicated significant enrichment of immune system process, regulation of blood circulation, apoptotic, regulation of secretion, regulation of blood vessel diameter, and so on. The molecular complex detection (MCODE) showed 17 genes were involved in the most significant module, and 6/17 genes were involved in vascular disorders. CytoHubba revealed the top 10 hub genes of DEGs, and four hub genes (App, F5, Fgg, and Gas6) related to vascular regulation were identified repeatedly by MCODE and cytoHubba. GeneMANIA analysis demonstrated functions of the four hub genes above and their associated molecules were primarily related to the regulation of circulation and coagulation. CMap analysis found several small molecular compounds to reverse the altered DEGs, including disulfiram, bumetanide, genistein, and so on. Our outputs could empower the novel potential targets to treat lacrimal angiopathy, diabetes dry eye, and other diabetes-related diseases.

Mechanism of Jianpi Qingchang Huashi Recipe in treating ulcerative colitis: A study based on network pharmacology and molecular docking

BACKGROUND

Ulcerative colitis (UC) is a refractory intestinal disease with alternating onset and remission and a long disease course, which seriously affects the health and quality of life of patients. The goal of treatment is to control clinical symptoms, induce and maintain remission, promote mucosal healing, and reduce recurrence. Clinical trials have shown unsatisfactory clinical response rates. As a supplementary alternative medicine, traditional Chinese medicine has a rich history and has shown good results in the treatment of UC. Because of the quality of herbal medicine and other factors, the curative effect of traditional Chinese medicine is not stable enough. The mechanism underlying the effect of Jianpi Qingchang Huashi Recipe (JPQCHSR) on inducing UC mucosal healing is not clear.

AIM

To investigate the potential mechanism of JPQCHSR for the treatment of UC based on network pharmacology and molecular docking.

METHODS

Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to extract the active components and action targets of JPQCHSR, and the target names were standardized and corrected through UniProt database. The related targets of UC were obtained through GeneCards database, and the intersection targets of drugs and diseases were screened by jvenn online analysis tool. The visual regulatory network of "Traditional Chinese medicine-active components-target-disease" was constructed using Cytoscape software, the protein interaction network was constructed using STRING database, and enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes pathways was conducted through R software. At last, the active components were docked with the core target through SYBYL-X 2.1.1 software.

RESULTS

Through database analysis, a total of 181 active components, 302 targets and 205 therapeutic targets were obtained for JPQCHSR. The key compounds include quercetin, luteolin, kaempferol, etc. The core targets involved STAT3, AKT1, TP53, MAPK1, MAPK3, JUN, TNF, etc. A total of 2861 items were obtained by GO enrichment analysis, and 171 items were obtained by KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis. The results of molecular docking showed that the key active components in JPQCHSR had certain affinity with the core target.

CONCLUSION

The treatment of UC with JPQCHSR is a complex process of multi-component, multi-target and multi-pathway regulation. The mechanism of this Recipe in the treatment of UC can be predicted through network pharmacology and molecular docking, so as to provide theoretical reference for it to better play its therapeutic role.

TCMNER and PubMed: A Novel Chinese Character-Level-Based Model and a Dataset for TCM Named Entity Recognition

Intelligent traditional Chinese medicine (TCM) has become a popular research field by means of prospering of deep learning technology. Important achievements have been made in such representative tasks as automatic diagnosis of TCM syndromes and diseases and generation of TCM herbal prescriptions. However, one unavoidable issue that still hinders its progress is the lack of labeled samples, i.e., the TCM medical records. As an efficient tool, the named entity recognition (NER) models trained on various TCM resources can effectively alleviate this problem and continuously increase the labeled TCM samples. In this work, on the basis of in-depth analysis, we argue that the performance of the TCM named entity recognition model can be better by using the character-level representation and tagging and propose a novel word-character integrated self-attention module. With the help of TCM doctors and experts, we define 5 classes of TCM named entities and construct a comprehensive NER dataset containing the standard content of the publications and the clinical medical records. The experimental results on this dataset demonstrate the effectiveness of the proposed module.

Anticancer Properties of Lobetyolin, an Essential Component of Radix Codonopsis (Dangshen)

Lobetyolin (LBT) is a polyacetylene glycoside found in diverse medicinal plants but mainly isolated from the roots of Codonopsis pilosula, known as Radix Codonopsis or Dangshen. Twelve traditional Chinese medicinal preparations containing Radix Codonopsis were identified; they are generally used to tonify spleen and lung Qi and occasionally to treat cancer. Here we have reviewed the anticancer properties of Codonopsis extracts, LBT and structural analogs. Lobetyolin and lobetyolinin are the mono- and bis-glucosylated forms of the polyacetylenic compound lobetyol. Lobetyol and LBT have shown activities against several types of cancer (notably gastric cancer) and we examined the molecular basis of their activity. A down-regulation of glutamine metabolism by LBT has been evidenced, contributing to drug-induced apoptosis and tumor growth inhibition. LBT markedly reduces both mRNA and protein expression of the amino acid transporter Alanine-Serine-Cysteine Transporter 2 (ASCT2). Other potential targets are proposed here, based on the structural analogy with other anticancer compounds. LBT and related polyacetylene glycosides should be further considered as potential anticancer agents, but more work is needed to evaluate their efficacy, toxicity, and risk-benefit ratio.

The Advantages of Connectivity Map Applied in Traditional Chinese Medicine

Amid the establishment and optimization of Connectivity Map (CMAP), the functional relationships among drugs, genes, and diseases are further explored. This biological database has been widely used to identify drugs with common mechanisms, repurpose existing drugs, discover the molecular mechanisms of unknown drugs, and find potential drugs for some diseases. Research on traditional Chinese medicine (TCM) has entered a new era in the wake of the development of bioinformatics and other subjects including network pharmacology, proteomics, metabolomics, herbgenomics, and so on. TCM gradually conforms to modern science, but there is still a torrent of limitations. In recent years, CMAP has shown its distinct advantages in the study of the components of TCM and the synergetic mechanism of TCM formulas; hence, the combination of them is inevitable.

Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort

Pancreatic adenocarcinoma (PAAD) is the most serious solid tumor type throughout the world. The present study aimed to identify novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. A total of 4777 differentially expressed genes (DEGs) were filtered, 2536 upregulated DEGs and 2241 downregulated DEGs. Weighted gene co-expression network analysis was then used and identified 12 modules, of which, blue module with the most significant enrichment result was selected. KEGG and GO enrichment analyses showed that all DEGs of blue module were enriched in EMT and PI3K/Akt pathway. Three hub genes (ITGB1, ITGB5, and OSMR) were determined as key genes with higher expression levels, significant prognostic value and excellent diagnostic efficiency for PAAD. Additionally, some small molecule drugs that possess the potential to treat PAAD were screened out, including thapsigargin (TG). Functional in vitro experiments revealed that TG repressed cell viability via inactivating the PI3K/Akt pathway in PAAD cells. Totally, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat PAAD.

HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine

Pharmacotranscriptomics has become a powerful approach for evaluating the therapeutic efficacy of drugs and discovering new drug targets. Recently, studies of traditional Chinese medicine (TCM) have increasingly turned to high-throughput transcriptomic screens for molecular effects of herbs/ingredients. And numerous studies have examined gene targets for herbs/ingredients, and link herbs/ingredients to various modern diseases. However, there is currently no systematic database organizing these data for TCM. Therefore, we built HERB, a high-throughput experiment- and reference-guided database of TCM, with its Chinese name as BenCaoZuJian. We re-analyzed 6164 gene expression profiles from 1037 high-throughput experiments evaluating TCM herbs/ingredients, and generated connections between TCM herbs/ingredients and 2837 modern drugs by mapping the comprehensive pharmacotranscriptomics dataset in HERB to CMap, the largest such dataset for modern drugs. Moreover, we manually curated 1241 gene targets and 494 modern diseases for 473 herbs/ingredients from 1966 references published recently, and cross-referenced this novel information to databases containing such data for drugs. Together with database mining and statistical inference, we linked 12 933 targets and 28 212 diseases to 7263 herbs and 49 258 ingredients and provided six pairwise relationships among them in HERB. In summary, HERB will intensively support the modernization of TCM and guide rational modern drug discovery efforts. And it is accessible through http://herb.ac.cn/.

Study of Components and Mechanism of Juechuang Against Platelet Aggregation Based on Network Pharmacology

Juechuang, a traditional Chinese herbal medicine, is originated from Rostellularia procumbens (L.) Nees. Many studies have shown that the ethyl acetate extract from Juechaung may inhibit platelet aggregation. However, the antiplatelet aggregation mechanism of Juechuang requires more systematic research. In this article, network pharmacology was used to explore the antiplatelet aggregation components and its antiplatelet aggregation mechanism. Different components were evaluated and screened by pharmacokinetic characteristics. The potential targets of active ingredients were predicted by a reverse pharmacophore matching method, and the targets were screened according to targets related to antiplatelet aggregation in the GeneCards database. Thus, an interaction network of component-target-pathway of Juechuang was generated using Cytoscape 3.2.1. software. Furthermore, the binding energy of relevant active components with key targets was calculated using a Lamarck genetic algorithm in the molecular docking calculations. Finally, the study identified 28 potentially active ingredients in Juechuang, providing further evidence that the active ingredients act on 277 targets, and 38 protein targets related to antiplatelet aggregation were screened. Through the Kyoto encyclopedia of genes and genome pathway enrichment analysis, we found that the mechanism of antiplatelet aggregation may be related to the Ras signaling pathway, platelet activation signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, etc. Via molecular docking of 2 targets, non-receptor tyrosine kinases(SRC) and MAPK were selected for molecular docking. By comparing the molecular docking results of Chinensinaphthol, Taiwanin E, Tuberculatin, Cycloeucalenol, and Justicidin B to the control drug, we found that those test molecules combined with targets and lead to high binding activity. These molecular docking results were also consistent with the literature values, and they helped identify the active ingredients and assured the reliability of the network analysis. This study may further provide a reference for the systematic study of the pharmacodynamic effect and the antiplatelet aggregation mechanism of Juechuang.

Predicting the Mechanism of the Analgesic Property of Yanhusuo Based on Network Pharmacology

Yanhusuo (Corydalis Rhizoma) extracts are widely used for the treatment of pain and inflammation. The effects of Yanhusuo in pain assays were assessed in a few studies. However, there are few studies on its analgesic mechanism. In this paper, network pharmacology was used to explore the analgesic components of Yanhusuo and its analgesic mechanism. The active components of Yanhusuo were screened by TCMSP database, combined with literature data. PharmMapper and GeneCards databases were used for screening the analgesic targets of the components. The protein interaction network diagram was drawn by String database and Cytoscape software, the gene ontology and KEGG pathway analyses of the target were performed by DAVID database, and the component–target–pathway interaction network diagram was further drawn by Cytoscape3.6.1 software. System Dock Web Site verified the molecular docking among components and targets. Finally, an interaction network of the component–target–pathway of Yanhusuo was constructed, and the functions and pathways were analyzed for preliminarily investigating the mechanism of Yanhusuo in analgesia. The results showed that the active components of analgesic in Yanhusuo were Corynoline, 13-methylpalmatrubine, dehydrocorydaline, saulatine, 2,3,9,10-tetramethoxy-13-methyl-5,6-dihydroisoquinolino[2,1-b]isoquinolin-8-on-e, and Capaurine. The mechanisms were involved in metabolic pathways, PI3k-Akt signaling pathway, pathways in cancer, and so on. The top 3 targets were NOS3, glucose-6-phosphate dehydrogenase, and glucose-6-phosphate isomerase in components-target-pathways network, and they were all enriched in metabolic pathways. Meanwhile the molecular docking showed that there was a high binding activity between the 6 components and the important target proteins, as a further certification for the subsequent network analysis. This study reveals the relationship of the components, targets, and pathways of active components in Yanhusuo, and provides new ideas and methods for further research on the analgesic mechanism of Yanhusuo.

Role of MicroRNAs Induced by Chinese Herbal Medicines Against Hepatocellular Carcinoma: A Brief Review

MicroRNAs (miRNAs) are highly conserved, noncoding small RNAs that regulate gene expression, and consequently several important functions including early embryo development, cell cycle, programmed cell death, cell differentiation, and metabolism. While there are no effective treatments available against hepatocellular carcinoma (HCC), some Chinese herbal medicines have been shown to regulate growth, differentiation, invasion, and metastasis of HCC. Many studies have shown that Chinese herbal medicines regulate the expression of miRNAs and this may be associated with their ability to control the development of HCC. In this article, the effects of Chinese herbal medicines on the expression of miRNAs and their functions in the regulation of HCC have been reviewed and discussed. miRNAs such as miRNA-221 and miRNA-222 mediated by Chinese herbal medicines may be good biomarkers and therapeutic targets for HCC.