Dual-channel hypergraph convolutional network for predicting herb-disease associations.

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study.

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic is an ongoing global health concern, and effective antiviral reagents are urgently needed. Traditional Chinese medicine theory-driven natural drug research and development (TCMT-NDRD) is a feasible method to address this issue as the traditional Chinese medicine formulae have been shown effective in the treatment of COVID-19. Huashi Baidu decoction (Q-14) is a clinically approved formula for COVID-19 therapy with antiviral and anti-inflammatory effects. Here, an integrative pharmacological strategy was applied to identify the antiviral and anti-inflammatory bioactive compounds from Q-14. Overall, a total of 343 chemical compounds were initially characterized, and 60 prototype compounds in Q-14 were subsequently traced in plasma using ultrahigh-performance liquid chromatography with quadrupole time-of-flight mass spectrometry. Among the 60 compounds, six compounds (magnolol, glycyrrhisoflavone, licoisoflavone A, emodin, echinatin, and quercetin) were identified showing a dose-dependent inhibition effect on the SARS-CoV-2 infection, including two inhibitors (echinatin and quercetin) of the main protease (Mpro), as well as two inhibitors (glycyrrhisoflavone and licoisoflavone A) of the RNA-dependent RNA polymerase (RdRp). Meanwhile, three anti-inflammatory components, including licochalcone B, echinatin, and glycyrrhisoflavone, were identified in a SARS-CoV-2-infected inflammatory cell model. In addition, glycyrrhisoflavone and licoisoflavone A also displayed strong inhibitory activities against cAMP-specific 3',5'-cyclic phosphodiesterase 4 (PDE4). Crystal structures of PDE4 in complex with glycyrrhisoflavone or licoisoflavone A were determined at resolutions of 1.54 Å and 1.65 Å, respectively, and both compounds bind in the active site of PDE4 with similar interactions. These findings will greatly stimulate the study of TCMT-NDRD against COVID-19.

Establishment and characterization of equine mammary organoids using a method translatable to other non-traditional model species.

Mammary organoid (MaO) models are only available for a few traditional model organisms, limiting our ability to investigate mammary gland development and cancer across mammals. This study established equine mammary organoids (EqMaOs) from cryopreserved mammary tissue, in which mammary tissue fragments were isolated and embedded into a 3D matrix to produce EqMaOs. We evaluated viability, proliferation and budding capacity of EqMaOs at different time points during culture, showing that although the number of proliferative cells decreased over time, viability was maintained and budding increased. We further characterized EqMaOs based on expression of stem cell, myoepithelial and luminal markers, and found that EqMaOs expressed these markers throughout culture and that a bilayered structure as seen in vivo was recapitulated. We used the milk-stimulating hormone prolactin to induce milk production, which was verified by the upregulation of milk proteins, most notably ß-casein. Additionally, we showed that our method is also applicable to additional non-traditional mammalian species, particularly domesticated animals such as cats, pigs and rabbits. Collectively, MaO models across species will be a useful tool for comparative developmental and cancer studies.

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.

TCMFP: a novel herbal formula prediction method based on network target's score integrated with semi-supervised learning genetic algorithms.

Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer's disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine.

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Rational Design of Tetrahedral Derivatives as Efficient Light-Emitting Materials Based on "Super Atom" Perspective.

Traditional semiconductor quantum dots of groups II-VI are key ingredients of next-generation display technology. Yet, the majority of them contain toxic heavy-metal elements, thus calling for alternative light-emitting materials. Herein, we have explored three novel categories of multicomponent compounds, namely, tetragonal II-III2-VI4 porous ternary compounds, cubic I2-II3-VI4 ternary compounds, and cubic I-II-III3-V4 quaternary compounds. This is achieved by judicious introduction of a "super atom" perspective and concurrently varying the solid-state lattice packing of involved super atoms or the population of surrounding counter cations. Based on first-principles calculations of 392 candidate materials with designed crystal structures, 53 highly stable materials have been screened. Strikingly, 34 of them are direct-bandgap semiconductors with emitting wavelengths covering the near-infrared and visible-light regions. This work provides a comprehensive database of highly efficient light-emitting materials, which may be of interest for a broad field of optoelectronic applications.

The triangular relationship between traditional Chinese medicines, intestinal flora, and colorectal cancer.

Over the past decade, colorectal cancer has reported a higher incidence in younger adults and a lower mortality rate. Recently, the influence of the intestinal flora in the initiation, progression, and treatment of colorectal cancer has been extensively studied, as well as their positive therapeutic impact on inflammation and the cancer microenvironment. Historically, traditional Chinese medicine (TCM) has been widely used in the treatment of colorectal cancer via promoted cancer cell apoptosis, inhibited cancer metastasis, and reduced drug resistance and side effects. The present research is more on the effect of either herbal medicine or intestinal flora on colorectal cancer. The interactions between TCM and intestinal flora are bidirectional and the combined impacts of TCM and gut microbiota in the treatment of colon cancer should not be neglected. Therefore, this review discusses the role of intestinal bacteria in the progression and treatment of colorectal cancer by inhibiting carcinogenesis, participating in therapy, and assisting in healing. Then the complex anticolon cancer effects of different kinds of TCM monomers, TCM drug pairs, and traditional Chinese prescriptions embodied in apoptosis, metastasis, immune suppression, and drug resistance are summarized separately. In addition, the interaction between TCM and intestinal flora and the combined effect on cancer treatment were analyzed. This review provides a mechanistic reference for the application of TCM and intestinal flora in the clinical treatment of colorectal cancer and paves the way for the combined development and application of microbiome and TCM.

Functions of Epimedin C in a zebrafish model of glucocorticoid-induced osteoporosis.

Epimedium is thought to enhance the integrity of tendons and bones, ease joint discomfort and rigidity and enhance kidney function. Although glucocorticoids are commonly used in clinical practice, the mechanism by which the active compound Epimedin C (EC) alleviates glucocorticoid-induced osteoporosis (GIOP) is not well understood. The therapeutic potential of EC in treating GIOP was evaluated using alizarin red S staining, calcein immersion and fluorescence imaging, and bone mineralization, bone mass accumulation and bone density in zebrafish larvae were determined. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the key signalling pathways related to bone development were identified. A protein-protein interaction network (PPIN) was constructed to identify osteoclast characteristic genes and the findings were verified using real-time quantitative PCR (RT-qPCR). The bone tissue damage caused by prednisolone was reduced by EC. It also altered physiological processes, improved bone density, boosted mineralization and increased bone mass and activity. Subsequent empirical investigations showed that EC impacted the major signalling pathways involved in bone development, such as osteoclast differentiation, oestrogen, MAPK, insulin resistance, PPAR and AMPK signalling pathways. It also decreased the expression of genes typical of osteoclasts. The results of our study uncover a previously unknown function of EC in controlling bone formation and emphasize the potential of EC as a therapeutic target. The osteoprotective effect of EC indicates its potential as a cost-effective strategy for treating GIOP.

Mechanism of Astragalus membranaceus (Huangqi, HQ) for treatment of heart failure based on network pharmacology and molecular docking.

Heart failure is a leading cause of death in the elderly. Traditional Chinese medicine, a verified alternative therapeutic regimen, has been used to treat heart failure, which is less expensive and has fewer adverse effects. In this study, a total of 15 active ingredients of Astragalus membranaceus (Huangqi, HQ) were obtained; among them, Isorhamnetin, Quercetin, Calycosin, Formononetin, and Kaempferol were found to be linked to heart failure. Ang II significantly enlarged the cell size of cardiomyocytes, which could be partially reduced by Quercetin, Isorhamnetin, Calycosin, Kaempferol, or Formononetin. Ang II significantly up-regulated ANP, BNP, ß-MHC, and CTGF expressions, whereas Quercetin, Isorhamnetin, Calycosin, Kaempferol or Formononetin treatment partially downregulated ANP, BNP, ß-MHC and CTGF expressions. Five active ingredients of HQ attenuated inflammation in Ang II-induced cardiomyocytes by inhibiting the levels of TNF-α, IL-1ß, IL-18 and IL-6. Molecular docking shows Isorhamnetin, Quercetin, Calycosin, Formononetin and Kaempferol can bind with its target protein ESR1 in a good bond by intermolecular force. Quercetin, Calycosin, Kaempferol or Formononetin treatment promoted the expression levels of ESR1 and phosphorylated ESR1 in Ang II-stimulated cardiomyocytes; however, Isorhamnetin treatment had no effect on ESR1 and phosphorylated ESR1 expression levels. In conclusion, our results comprehensively illustrated the bioactives, potential targets, and molecular mechanism of HQ against heart failure. Isorhamnetin, Quercetin, Calycosin, Formononetin and Kaempferol might be the primary active ingredients of HQ, dominating its cardioprotective effects against heart failure through regulating ESR1 expression, which provided a basis for the clinical application of HQ to regulate cardiac hypertrophy and heart failure.

Huanglian Jiedu decoction alleviates ischemia-induced cerebral injury in rats by mitigating NET formation and activiting GABAergic synapses.

Huanglian Jiedu decoction (HLJD) has been used to treat ischemic stroke in clinic. However, the detailed protective mechanisms of HLJD on ischemic stroke have yet to be elucidated. The aim of this study is to elucidate the underlying pharmacological mechanisms of HLJD based on the inhibition of neuroinflammation and the amelioration of nerve cell damage. A middle cerebral artery occlusion reperfusion (MCAO/R) model was established in rats and received HLJD treatment. Effects of HLJD on neurological function was assessed based on Bederson's score, postural reflex test and asymmetry score. 2, 3, 5-Triphenyltetrazolium chloride (TTC) staining, Hematein and eosin (HE) and Nissl staining were used to observe the pathological changes in brain. Then, transcriptomics was used to screen the differential genes in brain tissue in MCAO/R model rats following HLJD intervention. Subsequently, the effects of HLJD on neutrophil extracellular trap (NET) formation-related neuroinflammation, gamma-aminobutyric acid (GABA)ergic synapse activation, nerve cell damage and proliferation were validated using immunofluorescence, western blot and enzyme-linked immunosorbent assay (ELISA). Our results showed that HLJD intervention reduced the Bederson's score, postural reflex test score and asymmetry score in MCAO/R model rats. Pathological staining indicated that HLJD treatment decreased the cerebral infarction area, mitigated neuronal damage and increased the numbers of Nissl bodies. Transcriptomics suggested that HLJD affected 435 genes in MCAO/R rats. Among them, several genes involving in NET formation and GABAergic synapses pathways were dysregulated. Subsequent experimental validation showed that HLJD reduced the MPO+CitH3+ positive expression area, reduced the protein expression of PAD4, p-P38/P38, p-ERK/ERK and decreased the levels of IL-1ß, IL-6 and TNF-α, reversed the increase of Iba1+TLR4+, Iba1+p65+ and Iba1+NLRP3+ positive expression area in brain. Moreover, HLJD increased GABA levels, elevated the protein expression of GABRG1 and GAT3, decreased the TUNEL positive expression area and increased the Ki67 positive expression area in brain. HLJD intervention exerts a multifaceted positive impact on ischemia-induced cerebral injury in MCAO/R rats. This intervention effectively inhibits neuroinflammation by mitigating NET formation, and concurrently improves nerve cell damage and fosters nerve cell proliferation through activating GABAergic synapses.

Zhi-zi-chi decoction mitigates depression by enhancing lncRNA Six3os1 expression and promoting histone H3K4 methylation at the BDNF promoter.

Traditional Chinese medicine, particularly Zhi-zi-chi decoction (ZZCD), is gaining recognition as a potential treatment for depression. This study aimed to uncover the molecular mechanisms behind ZZCD's antidepressant effects, focusing on lncRNA Six3os1 and histone H3K4 methylation at the BDNF promoter. Network pharmacology and in vivo experiments were conducted to identify ZZCD targets and evaluate its impact on depression-related behaviours and neuron injury. The role of Six3os1 in recruiting KMT2A to the BDNF promoter and its effects on oxidative stress and neuron injury were investigated. ZZCD reduced depression-like behaviours and neuron injury in mice subjected to chronic stress. It upregulated Six3os1, which facilitated KMT2A recruitment to the BDNF promoter, leading to increased histone H3K4 methylation and enhanced BDNF expression. ZZCD also inhibited CORT-induced neuron injury, inflammatory response and oxidative stress in vitro. ZZCD's antidepressant properties involve Six3os1 upregulation, which exerts neuroprotective effects by inhibiting oxidative stress and neuron injury, thereby alleviating depressive symptoms. Targeting Six3os1 upregulation may offer a potential therapeutic intervention for depression.

Plant-Derived Compounds: A Promising Tool for Dental Caries Prevention.

There is a growing shift from the use of conventional pharmaceutical oral care products to the use of herbal extracts and traditional remedies in dental caries prevention. This is attributed to the potential environmental and health implications of contemporary oral products. This comprehensive review aims at the analysis of plant-derived compounds as preventive modalities in dental caries research. It focuses on data collected from 2019 until recently, trying to emphasize current trends in this topic. The research findings suggest that several plant-derived compounds, either aqueous or ethanolic, exhibit notable antibacterial effects against Streptococcus mutans and other bacteria related to dental caries, with some extracts demonstrating an efficacy comparable to that of chlorhexidine. Furthermore, in vivo studies using plant-derived compounds incorporated in food derivatives, such as lollipops, have shown promising results by significantly reducing Streptococcus mutans in high-risk caries children. In vitro studies on plant-derived compounds have revealed bactericidal and bacteriostatic activity against S. mutans, suggesting their potential use as dental caries preventive agents. Medicinal plants, plant-derived phytochemicals, essential oils, and other food compounds have exhibited promising antimicrobial activity against oral pathogens, either by their anti-adhesion activity, the inhibition of extracellular microbial enzymes, or their direct action on microbial species and acid production. However, further research is needed to assess their antimicrobial activity and to evaluate the cytotoxicity and safety profiles of these plant-derived compounds before their widespread clinical use can be recommended.

Genetic diversities in wild and cultivated populations of the two closely-related medical plants species, Tripterygium Wilfordii and T. Hypoglaucum (Celastraceae).

BACKGROUND: The sustainable supply of medicinal plants is important, and cultivating and domesticating them has been suggested as an optimal strategy. However, this can lead to a loss of genetic diversity. Tripterygium wilfordii Hook. f. is a medicinal plant commonly used in traditional Chinese medicine, but its wild populations are dwindling due to excessive harvesting. To protect the species and meet the increasing demand, it is urgent to cultivate it on a large scale. However, distinguishing between T. wilfordii and T. hypoglaucum, two similar species with different medicinal properties, is challenging. Therefore, it is crucial to understand the genetic diversity and population structure of these species for their sustainable utilization. RESULTS: In this study, we investigated the genetic diversity and population structure of the two traditional medicinal semiwoody vines plant species, Tripterygium wilfordii and T. hypoglaucum, including wild and cultivated populations using chloroplast DNA (cpDNA) sequences and microsatellite loci. Our results indicated that the two species maintain a high level of genetic divergence, indicating possible genetic bases for the different contents of bioactive compounds of the two species. T. wilfordii showed lower genetic diversity and less subdivided population structures of both markers than T. hypoglaucum. The potential factors in shaping these interesting differences might be differentiated pollen-to-seed migration rates, interbreeding, and history of population divergence. Analyses of cpDNA and microsatellite loci supported that the two species are genetically distinct entities. In addition, a significant reduction of genetic diversity was observed for cultivated populations of the two species, which mainly resulted from the small initial population size and propagated vegetative practice during their cultivation. CONCLUSION: Our findings indicate significant genetic divergence between T. wilfordii and T. hypoglaucum. The genetic diversity and population structure analyses provide important insights into the sustainable cultivation and utilization of these medicinal plants. Accurate identification and conservation efforts are necessary for both species to ensure the safety and effectiveness of crude drug use. Our study also highlighted the importance of combined analyses of different DNA markers in addressing population genetics of medicinal plants because of the contrasts of inheritance and rates of gene flow. Large-scale cultivation programs should consider preserving genetic diversity to enhance the long-term sustainability of T. wilfordii and T. hypoglaucum. Our study proposed that some populations showed higher genetic diversity and distinctness, which can be considered with priority for conservation and as the sources for future breeding and genetic improvement.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India.

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Signaling repurposable drug combinations against COVID-19 by developing the heterogeneous deep herb-graph method.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spurred a boom in uncovering repurposable existing drugs. Drug repurposing is a strategy for identifying new uses for approved or investigational drugs that are outside the scope of the original medical indication. MOTIVATION: Current works of drug repurposing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are mostly limited to only focusing on chemical medicines, analysis of single drug targeting single SARS-CoV-2 protein, one-size-fits-all strategy using the same treatment (same drug) for different infected stages of SARS-CoV-2. To dilute these issues, we initially set the research focusing on herbal medicines. We then proposed a heterogeneous graph embedding method to signaled candidate repurposing herbs for each SARS-CoV-2 protein, and employed the variational graph convolutional network approach to recommend the precision herb combinations as the potential candidate treatments against the specific infected stage. METHOD: We initially employed the virtual screening method to construct the 'Herb-Compound' and 'Compound-Protein' docking graph based on 480 herbal medicines, 12,735 associated chemical compounds and 24 SARS-CoV-2 proteins. Sequentially, the 'Herb-Compound-Protein' heterogeneous network was constructed by means of the metapath-based embedding approach. We then proposed the heterogeneous-information-network-based graph embedding method to generate the candidate ranking lists of herbs that target structural, nonstructural and accessory SARS-CoV-2 proteins, individually. To obtain precision synthetic effective treatments forvarious COVID-19 infected stages, we employed the variational graph convolutional network method to generate candidate herb combinations as the recommended therapeutic therapies. RESULTS: There were 24 ranking lists, each containing top-10 herbs, targeting 24 SARS-CoV-2 proteins correspondingly, and 20 herb combinations were generated as the candidate-specific treatment to target the four infected stages. The code and supplementary materials are freely available at https://github.com/fanyang-AI/TCM-COVID19.

Potential COVID-19 remedies from repurposed drugs and herbal small RNAs.

To date, SARS-CoV-2 has caused millions of deaths, but the choice of treatment is limited. We previously established a platform for identifying Food and Drug Administration (FDA)-approved repurposed drugs for avian influenza A virus infections that could be used for coronavirus disease 2019 (COVID-19) treatment. In this study, we analyzed blood samples from two cohorts of 63 COVID-19 patients, including 19 patients with severe disease. Among the 39 FDA-approved drugs we identified for COVID-19 therapy in both cohorts, 23 drugs were confirmed by literature mining data, including 14 drugs already under COVID-19 clinical trials and 9 drugs reported for COVID-19 treatments, suggesting the remaining 16 FDA-approved drugs may be candidates for COVID-19 therapy. Additionally, we previously reported that herbal small RNAs (sRNAs) could be effective components in traditional Chinese medicine (TCM) for treating COVID-19. Based on the abundance of sRNAs, we screened the 245 TCMs in the Bencao (herbal) sRNA Atlas that we had previously established, and we found that the top 12 TCMs for COVID-19 treatment was consistent across both cohorts. We validated the efficiency of the top 30 sRNAs from each of the top 3 TCMs for COVID-19 treatment in poly(I:C)-stimulated human non-small cell lung cancer cells (A549 cells). In conclusion, our study recommends potential COVID-19 remedies using FDA-approved repurposed drugs and herbal sRNAs from TCMs.

COX-2/PTGS2-targeted herbal-derived oligonucleotide drug HQi-sRNA-2 was effective in spontaneous mouse lung cancer model.

In 2020, the number of deaths caused by lung cancer worldwide reached 1,796,144, making it the leading cause of cancer-related deaths. Cyclooxygenase-2/prostaglandin endoperoxide synthase 2 (COX-2/PTGS2) is overexpressed in lung cancer, which promotes tumor proliferation, invasion, angiogenesis, and resistance to apoptosis. Here, we report that the oligonucleotide drug HQi-sRNA-2 from Traditional Chinese Medicine Huangqin targeting COX-2/PTGS2 significantly inhibited proliferation, migration, and invasion and induced apoptosis in the human lung cancer cell line NCI-H460. Oral delivery of HQi-sRNA-2 bencaosomes prolonged survival, reduced tumor burden, and maintained weight in a spontaneous mouse lung cancer model. Compared with paclitaxel, HQi-sRNA-2 may be less toxic and have approximately equal efficacy in reducing tumor burden. Our previous studies reported that herbal small RNAs (sRNAs) are functional medical components. Our data suggest that sphingosine (d18:1)-HQi-sRNA-2 bencaosomes, targeting COX-2/PTGS2 and downregulating the PI3K and AKT signaling pathways, may provide novel therapeutics for lung cancer.

Bencaosome [16:0 Lyso PA+XLGB28-sRNA] improves osteoporosis by simultaneously promoting osteogenesis and inhibiting osteoclastogenesis in mice.

Osteoporosis (OP) is a systemic metabolic bone disease resulting in reduced bone strength and increased susceptibility to fractures, making it a significant public health and economic problem worldwide. The clinical use of anti-osteoporosis agents is limited because of their serious side effects or the high cost of long-term use. The Xianlinggubao (XLGB) formula is an effective traditional Chinese herbal medicine commonly used in orthopedics to treat osteoporosis; however, its mechanism of action remains unclear. In this study, we screened 40 small RNAs derived from XLGB capsules and found that XLGB28-sRNA targeting TNFSF11 exerted a significant anti-osteoporosis effect in vitro and in vivo by simultaneously promoting osteogenesis and inhibiting osteoclastogenesis. Oral administration of bencaosome [16:0 Lyso PA+XLGB28-sRNA] effectively improved bone mineral density and reduced the damage to the bone microstructure in mice. These results suggest that XLGB28-sRNA may be a novel oligonucleotide drug that promotes osteogenesis and inhibits osteoclastogenesis in mice.