Identification of the biological targets of a compound is of paramount importance for the exploration of the mechanism of action of drugs and for the development of novel drugs. A concept of the Connectivity Map (CMap) was previously proposed to connect genes, drugs, and disease states based on the common gene-expression signatures. For a new query compound, the CMap-based method can infer its potential targets by searching similar drugs with known targets (reference drugs) and measuring the similarities into their specific transcriptional responses between the query compound and those reference drugs. However, the available methods are often inefficient due to the requirement of the reference drugs as a medium to link the query agent and targets. Here, we developed a general procedure to extract target-induced consensus gene modules from the transcriptional profiles induced by the treatment of perturbagens of a target. A specific transcriptional gene module pair (GMP) was automatically identified for each target and could be used as a direct target signature. Based on the GMPs, we built the target network and identified some target gene clusters with similar biological mechanisms. Moreover, a gene module pair-based target identification (GMPTI) approach was proposed to predict novel compound–target interactions. Using this method, we have discovered novel inhibitors for three PI3K pathway proteins PI3Kα/β/δ, including PU-H71, alvespimycin, reversine, astemizole, raloxifene HCl, and tamoxifen.
Abstract One of the most difficult problems that hinder the development and application of herbal medicine is how to illuminate the global effects of herbs on the human body. Currently, the chemo-centric network pharmacology methodology regards herbs as a mixture of chemical ingredients and constructs the ‘herb-compound-target-disease’ connections based on bioinformatics methods, to explore the pharmacological effects of herbal medicine. However, this approach is severely affected by the complexity of the herbal composition. Alternatively, gene-expression profiles induced by herbal treatment reflect the overall biological effects of herbs and are suitable for studying the global effects of herbal medicine. Here, we develop an online transcriptome-based multi-scale network pharmacology platform (TMNP) for exploring the global effects of herbal medicine. Firstly, we build specific functional gene signatures for different biological scales from molecular to higher tissue levels. Then, specific algorithms are designed to measure the correlations of transcriptional profiles and types of gene signatures. Finally, TMNP uses pharmacotranscriptomics of herbal medicine as input and builds associations between herbs and different biological scales to explore the multi-scale effects of herb medicine. We applied TMNP to a single herb Astragalus membranaceus and Xuesaitong injection to demonstrate the power to reveal the multi-scale effects of herbal medicine. TMNP integrating herbal medicine and multiple biological scales into the same framework, will greatly extend the conventional network pharmacology model centering on the chemical components, and provide a window for systematically observing the complex interactions between herbal medicine and the human body. TMNP is available at http://www.bcxnfz.top/TMNP.
Abstract Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference, clustered regularly interspaced short palindromic repeat, and overexpression. TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26 945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.
Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Here, we show that TranscriptionNet, a deep learning model that integrates multiple biological networks to systematically predict transcriptional profiles to three types of genetic perturbations based on transcriptional profiles induced by genetic perturbations in the L1000 project: RNA interference (RNAi), clustered regularly interspaced short palindromic repeat (CRISPR) and overexpression (OE). TranscriptionNet performs better than existing approaches in predicting inducible gene expression changes for all three types of genetic perturbations. TranscriptionNet can predict transcriptional profiles for all genes in existing biological networks and increases perturbational gene expression changes for each type of genetic perturbation from a few thousand to 26,945 genes. TranscriptionNet demonstrates strong generalization ability when comparing predicted and true gene expression changes on different external tasks. Overall, TranscriptionNet can systemically predict transcriptional consequences induced by perturbing genes on a genome-wide scale and thus holds promise to systemically detect gene function and enhance drug development and target discovery.
Objective: Illumination of the integrative effects of herbs in a formula is a bottleneck that limits the development of traditional Chinese medicine (TCM). In the present study, we developed a transcriptome-based multi-scale network pharmacology model to explore the combined effects of different herbs. Materials and Methods: First, we curated gene signatures at different biological scales, from the molecular to higher tissue levels, including tissues, cells, pathological processes, biological processes, pathways, and targets. Second, using the Xiexin Tang (XXT) formula as an example, we collected transcriptomic data in response to the treatment of XXT or its three compositive herbs on Michigan cancer foundation7 cells. Third, we linked each herbal drug to different biological scales by calculating the correlation scores between herb-induced gene expression profiles and gene signatures. Finally, the combined mechanisms of the three constituent herbs in XXT were deciphered by comparing their multi-scale effects with those of the formula. Results: The results showed that although XXT or single herbs regulated a large number of signatures on each biological scale, the phenotypic effects of these herbal drugs are concentrated onto the “Blood” tissue, types of hemocytes, and hemorrhagic injury-related pathological processes. At the molecular level, these herbs consistently regulate processes such as the cell cycle and blood coagulation-related pathways, as well as protein targets related to the immunoinflammatory response and blood coagulation, such as proteinase-activated receptor 2, integrin beta-3, inhibitor of nuclear factor kappa-B kinase subunit beta, and coagulation factor XII. The analysis of the combinational modes demonstrated that different herbs can cooperate by acting on the same objects and/or regulating different objects in related functions, and cooperative behaviors change at different biological scales. Conclusions: Our model can dissect the combined effects of herbal formulae from a multi-scale perspective and should be beneficial for the development and exploitation of TCM.
A natural α-1,6-glucan named BBWPW was identified from black beans. Cell viability assay showed that BBWPW inhibited the proliferation of different cancer cells, especially HeLa cells. Flow cytometry analysis indicated that BBWPW suppressed the HeLa cell cycle in the G2/M phase. Consistently, RT-PCR experiments displayed that BBWPW significantly impacts the expression of four marker genes related to the G2/M phase, including p21, CDK1, Cyclin B1, and Survivin. To explore the molecular mechanism of BBWPW to induce cell cycle arrest, a transcriptome-based target inference approach was utilized to predict the potential upstream pathways of BBWPW and it was found that the PI3K-Akt and MAPK signal pathways had the potential to mediate the effects of BBWPW on the cell cycle. Further experimental tests confirmed that BBWPW increased the expression of BAD and AKT and decreased the expression of mTOR and MKK3. These results suggested that BBWPW could regulate the PI3K-Akt and MAPK pathways to induce cell cycle arrest and ultimately inhibit the proliferation of HeLa cells, providing the potential of the black bean glucan to be a natural anticancer drug.
An alkali-extracted polysaccharide (PCAPS1) was isolated and purified from the Poria cocos. Our results proved that PCAPS1 was a neutral polysaccharide with a molecular weight of 11.5 kDa. The monosaccharide composition, methylation and NMR analysis results displayed that the polysaccharide was mostly comprised of β-1,3-glucan with 1,4 and 1,6 branches. The Immune activity and mechanism of PCAPS1 were evaluated in RAW264.7 cells. The enzyme-linked immunosorbent assay (ELISA) analysis revealed that PCAPS1 increased the tumor necrosis factor-α (TNF-α) secretion. RNA-sequencing data analysis suggested that PCAPS1 activated macrophages by the classic NF-κB pathway. Real-time quantitative polymerase chain reaction (RT-qPCR) analysis confirmed that PCAPS1 enhanced mRNA expression levels of TNF-α and nuclear factor κB (NF-κB) in RAW264.7 cells. Simultaneously, the fluorescence nuclear transport experiment showed that PCAPS1 activated RAW264.7 cells by inducing the NF-κB p65 translocation. Our results indicated that PCAPS1-induced TNF-α expression was mediated via the NF-κB signaling pathway.
Abstract Objective: Patients with coronavirus disease 2019 (COVID-19) experience various symptoms such as fever, cough, fatigue, and headache. At present, only two traditional over-the-counter medications, acetaminophen, and ibuprofen, are recommended for treating COVID-19 symptoms. However, there is an urgent need to discover safer remedies, potentially found in daily food items, that can be used for long-term prevention of COVID-19 symptoms. This study aimed to explore safe natural products capable of alleviating COVID-19 symptoms. Materials and Methods: We developed a Transcriptome-based Functional Gene Module Reference (TFGMR) approach, utilizing gene expression data from natural products and gene modules associated with COVID-19. Our hypothesis was that candidate natural products would significantly inhibit the expression of genes linked to COVID-19 symptoms without enhancing the expression of genes related to severe acute respiratory syndrome coronavirus 2 invasion. Results: Using the TFGMR approach, we identified 109 natural products with the potential to alleviate COVID-19 symptoms, with 15 of them having experimental evidence supporting their efficacy. These natural products consist of three daily food items – olive oil, nuts, and a mixture of vegetables, fruits, and yogurt; 43 functional foods, such as Fructus Gardeniae and Flos Lonicerae; as well as 63 natural drugs such as Plantamajoside and Safflomin A. These findings suggest that incorporating these three daily food items into one’s diet may contribute to the prevention of COVID-19. Conclusion: The study underscores the importance of recommending functional foods based on robust scientific evidence supporting their efficacy. The integration of diverse technological approaches holds promise in identifying safe remedies that could aid in the prevention and alleviation of COVID-19 symptoms, providing new opportunities for their everyday use.
Identification of drug–target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug–target interaction prediction. Here, we propose DrugMAN, a deep learning model for predicting drug–target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug–target prediction. DrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN's learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug–target interactions and can be a powerful tool for drug discovery and drug repurposing.
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