Network-Based Combinatorial CRISPR-Cas9 Screens Identify Synergistic Modules in Human Cells

Network-based identification and mechanism exploration of active ingredients against Alzheimer's disease via targeting endoplasmic reticulum stress from traditional chinese medicine

Alzheimer's disease is a neurodegenerative disease that leads to dementia and poses a serious threat to the health of the elderly. Traditional Chinese medicine (TCM) presents as a promising novel therapeutic therapy for preventing and treating dementia. Studies have shown that natural products derived from kidney-tonifying herbs can effectively inhibit AD. Furthermore, endoplasmic reticulum (ER) stress is a critical factor in the pathology of AD. Regulation of ER stress is a crucial approach to prevent and treat AD. Thus, in this study, we first collected kidney-tonifying herbs, integrated chemical ingredients from multiple TCM databases, and constructed a comprehensive drug-target network. Subsequently, we employed the endophenotype network (network proximity) method to identify potential active ingredients in kidney-tonifying herbs that prevented AD via regulating ER stress. By combining the predicted outcomes, we discovered that 32 natural products could ameliorate AD pathology via regulating ER stress. After a comprehensive evaluation of the multi-network model and systematic pharmacological analyses, we further selected several promising compounds for in vitro testing in the APP-SH-SY5Y cell model. Experimental results showed that echinacoside and danthron were able to effectively reduce ER stress-mediated neuronal apoptosis by inhibiting the expression levels of BIP, p-PERK, ATF6, and CHOP in APP-SH-SY5Y cells. Overall, this study utilized the endophenotype network to preliminarily decipher the effective material basis and potential molecular mechanism of kidney-tonifying Chinese medicine for prevention and treatment of AD.

Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective

OBJECTIVE

Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies.

METHODS

We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC.

RESULTS

Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets.

CONCLUSIONS

Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.

Identification of potential anti-tumor targets and mechanisms of HuaChanSu injection using network pharmacology and cytological experiments in Breast cancer

HuaChanSu (HCS) or Cinobufacini injection is an aqueous extract of the dried skin of Bufo bufo gargarigans, and has anti-tumor effects. The aim of this study was to evaluate the possible therapeutic effect of HCS against breast cancer (BRCA) using cytology, network pharmacology, and molecular biology approaches. The half-inhibitory concentration (IC50) of HCS in the BRCA cells was determined by cytotoxicity assay, and were accordingly treated with high and low doses HCS in the TUNEL and scratch assays. The potential targets of HCS in the BRCA cells were identified through functional enrichment analysis and protein-protein interaction (PPI) networks, and verified by molecular docking. The expression levels of key signaling pathways-related proteins in HCS-treated BRCA cells by western blotting. HCS inhibited the proliferation and migration of MCF-7 and MDA-MB-231 cells, and induced apoptosis in a dose-dependent manner. Furthermore, we screened 289 core HCS targets against BRCA, which were primarily enriched in the PI3K-AKT, MAPK chemokines, and other. signaling pathways. In addition, PIK3CA, PIK3CD, and MTOR were confirmed as HCS targets by molecular docking. Consistent with this, we observed a reduction in the expression levels of phosphorylated PI3K, AKT, and MTOR in the HCS-treated BRCA cells. Taken together, our findings suggest that HCS inhibits the growth of BRCA cells by targeting the PI3K-AKT pathway, and warrants further investigation as a therapeutic agent for treating patients with BRCA.

Network analysis to explore the anti-senescence mechanism of Jinchan Yishen Tongluo Formula (JCYSTLF) in diabetic kidneys

Background

The Jinchan Yishen Tongluo Formula (JCYSTLF) has the effect of delaying senescence in diabetic kidneys. However, the mechanism is not clear.

Purpose

Combination methods to investigate the anti-senescence mechanism of JCYSTLF in diabetic kidneys.

Methods

The main compounds of JCYSTLF were characterized by LC-MS/MS, and the anti-senescence targets of JCYSTLF were screened via network analysis. Then, we performed in vivo and in vitro experiments to validate the results.

Results

The target profiles of compounds were obtained by LC-MS/MS to characterize the primary function of JCYSTLF. Senescence was identified as a key biological functional module of JCYSTLF in the treatment of DN via constructing compounds-target-biological network analysis. Further analysis of senescence-related targets recognized the HIF-1α/autophagy pathway as the core anti-senescence mechanism of JCYSTLF in diabetic kidneys. Animal experiments showed, in comparison with valsartan, JCYSTLF showed an improvement in urinary albumin and renal pathological damage. JCYSTLF enhanced the ability of diabetic kidneys to clear senescence-related proteins via regulating autophagy confirmed by autophagy inhibitor CQ. However, HIF-1α inhibitor 2-ME weakened the role of JCYSLTF in regulating autophagy in diabetic kidneys. Meanwhile, over-expressed HIF-1α in HK-2 cells decreased the levels of SA-β-gal, p21 and p53 induced by AGEs. Upregulated HIF-1α could reverse the blocking of autophagy induced by AGEs in HK-2 cells evaluated by ptfLC3.

Conclusion

We provided in vitro and in vivo evidence for the anti-senescence role of JCYSTLF in regulating the HIF-1α/autophagy pathway.

Uncovering the pharmacological mechanism of Shou Tai Wan on recurrent spontaneous abortion: A integrated pharmacology strategy-based research

ETHNOPHARMACOLOGICAL RELEVANCE

Shou Tai Wan (STW), a traditional Chinese medicine formula, has been historically used for the treatment of recurrent spontaneous abortion (RSA). Despite its long-standing usage, the exact mechanism underlying the therapeutic effects of STW remains unclear in the existing literature.

AIMS OF THIS STUDY

To explore the Pharmacological Mechanism of STW on RSA.

METHODS

A network pharmacological methodology was utilized to predict the active compounds and potential targets of STW, collect the RSA targets and other human proteins of STW, and analyze the STW related networks. The animal experiments were also performed to validate the effect of STW on RSA.

RESULTS

The results of network analysis showed that STW may regulate PI3K/AKT, MAPK, FoxO signaling pathways and so on. Animal experiment established the RSA model with CBA/J × DBA/2 mice. It was found that STW can reduce the embryo absorption rate of RSA group (p < 0.05) and balance the expression of Th 1/Th2 type cytokines compared with the model group. After 14 days of administration, the decidual and placental tissues were taken and the CD4+ T cells were isolated, and the phosphorylation level of signaling pathway was detected by Springbio720 antibody microarray. This experiment found that STW can significantly up-regulate the phosphorylation levels of STAT3 and STAT6 proteins in the STAT signaling pathway, and down-regulating the phosphorylation level of STAT1 protein. STW also significantly up-regulated the phosphorylation levels of Raf1, A-Raf, Ask1, Mek1, Mek2, JKK1, ERK1, ERK2, c-fos, c-Jun and CREB proteins in the MAPK signaling pathway, and down-regulate the phosphorylation levels of MEK6 and IKKb proteins. Compared with the RSA group, the STW group increased the expression levels of ERK1/2 mRNA and proteins and p-ERK1/2 proteins, and there was a statistical difference (p < 0.05). This is consistent with the chip results.

CONCLUSION

STW may achieve therapeutic effects by interfering with the signaling pathways, biological processes and targets discovered in this study. It provides a new perspective for revealing the immunological mechanism of STW in the treatment of RSA, and also provides a theoretical basis for the clinical use of STW in the treatment of RSA.

Magnolol and 5-fluorouracil synergy inhibition of metastasis of cervical cancer cells by targeting PI3K/AKT/mTOR and EMT pathways

Objective

This study is designed to investigate the mode of action of the synergistic effect of 5-fluorouracil (5-FU) and magnolol against cervical cancer.

Methods

Network pharmacological approach was applied to predict the molecular mechanism of 5-FU combined with magnolol against cervical cancer. CCK-8 assay, colony formation assay, immunofluorescence staining, adhesion assay, wound healing mobility assay, cell migration and invasion assay and Western blot analysis were conducted to validate the results of in silico study.

Results

Phosphatidylinositol 3 kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway was identified as the key pathway in silico study. The experimental results showed that 5-FU combined with magnolol strongly inhibited cervical cancer cell proliferation, induced the morphological change of HeLa cells by down-regulating the expression of α-actinin, tensin-2 and vinculin. Moreover, magnolol enhanced inhibitory effect of 5-FU on the cell adhesion, migration and invasion. The phosphorylation of AKT and PI3K and the expression of mTOR were strongly inhibited by the combination of 5-FU and magnolol. Moreover, the expression of E-cadherin and β-catenin was upregulated and the expression of Snail, Slug and vimentin was down-regulated by the 5-FU together with magnolol.

Conclusion

Taken together, this study suggests that 5-FU combined with magnolol exerts a synergistic anti-cervical cancer effect by regulating the PI3K/AKT/mTOR and epithelial-mesenchymal transition (EMT) signaling pathways.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

The relationship between TGF-β1 and cognitive function in the brain

Transforming growth factor-β1 (TGF-β1), a multifunctional cytokine, plays a pivotal role in synaptic formation, plasticity, and neurovascular unit regulation. This review highlights TGF-β1's potential impact on cognitive function, particularly in the context of neurodegenerative disorders. However, despite the growing body of evidence, a comprehensive understanding of TGF-β1's precise role remains elusive. Further research is essential to unravel the complex mechanisms through which TGF-β1 influences cognitive function and to explore therapeutic avenues for targeting TGF-β1 in neurodegenerative conditions. This investigation sheds light on TGF-β1's contribution to cognitive function and offers prospects for innovative treatments and interventions. This review delves into the intricate relationship between TGF-β1 and cognitive function.

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.

Uncovering the mechanisms of Yi Qi Tong Qiao Pill in the treatment of allergic rhinitis based on Network target analysis

OBJECTIVE

The purpose of this study is to reveal the mechanism of action of Yi Qi Tong Qiao Pill (YQTQP) in the treatment of allergic rhinitis (AR), as well as establish a paradigm for the researches on traditional Chinese medicine (TCM) from systematic perspective.

METHODS

Based on the data collected from TCM-related and disease-related databases, target profiles of compounds in YQTQP were calculated through network-based algorithms and holistic targets of TQTQP was constructed. Network target analysis was performed to explore the potential mechanisms of YQTQP in the treatment of AR and the mechanisms were classified into different modules according to their biological functions. Besides, animal and clinical experiments were conducted to validate our findings inferred from Network target analysis.

RESULTS

Network target analysis showed that YQTQP targeted 12 main pathways or biological processes related to AR, represented by those related to IL-4, IFN-γ, TNF-α and IL-13. These results could be classified into 3 biological modules, including regulation of immune and inflammation, epithelial barrier disorder and cell adhesion. Finally, a series of experiments composed of animal and clinical experiments, proved our findings and confirmed that YQTQP could improve related symptoms of AR, like permeability of nasal mucosa epithelium.

CONCLUSION

A combination of Network target analysis and the experimental validation indicated that YQTQP was effective in the treatment of AR and might provide a new insight on revealing the mechanism of TCM against diseases. Trial registration Name of the registry: Chinese Clinical Trial Registry: Trial registration number: ChiCTR-TRC-13,003,137: Date of registration: Registered 29 March 2013 - Retrospectively registered: URL of trial registry record: https://www.chictr.org.cn/showproj.html?proj=6422 .

The potential mechanism of qinghua quyu jianpi decoction in the treatment of ulcerative colitis based on network pharmacology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Ulcerative colitis (UC) is a chronic and recurrent inflammation of the gastrointestinal tract. Following the idea of herbal property and compatibility, a traditional Chinese medicine (TCM) formula consists of a number of TCM herbs. Qinghua Quyu Jianpi Decoction (QQJD) has been clinically proven to be effective in treating UC, however, its therapeutic mechanism has not been fully elucidated.

AIM OF STUDY

Here, we used network pharmacology analysis and ultra-performance liquid chromatography-tandem mass spectrometry to predict the mechanism of action of QQJD, and then validated our predictions through in vivo and in vitro experiments.

MATERIALS AND METHODS

First, based on a number of datasets, relationship network diagrams between QQJD and UC were created. The target network for the QQJD-UC intersection genes was then built, and KEGG analysis was carried out to identify a potential pharmacological mechanism. Finally, the results of the previous prediction were validated in dextran sulfate sodium salt (DSS) induced UC mice and a cellular inflammatory model.

RESULTS

Network pharmacology results suggested that QQJD may play a role in repairing intestinal mucosa by activating Wnt pathway. In vivo experiments have shown that QQJD can significantly reduce weight loss, disease activity index (DAI) score, improve colon length, and effectively repair the tissue morphology of UC mice. In addition, we also found that QQJD can activate the Wnt pathway to promote epithelial cell renewal, reduce apoptosis, and repair the mucosal barrier. To further understand how QQJD promotes cell proliferation in DSS-induced Caco-2 cells, we performed a study in vitro experiment. We were surprised to find that QQJD activated the Wnt pathway by inducing nuclear translocation of β-catenin, accelerating the cell cycle and promoting cell proliferation in vitro.

CONCLUSION

Taken together, network pharmacology and experiments showed that QQJD achieves mucosal healing and restores the colonic epithelium barrier by activating Wnt/β-catenin signaling, regulating cell cycle progression, and promoting the proliferation of epithelial cells.

Dual synergistic inhibition of COX and LOX by potential chemicals from Indian daily spices investigated through detailed computational studies

Cyclooxygenase (COX) and Lipoxygenase (LOX) are essential enzymes for arachidonic acid (AA) to eicosanoids conversion. These AA-derived eicosanoids are essential for initiating immunological responses, causing inflammation, and resolving inflammation. Dual COX/5-LOX inhibitors are believed to be promising novel anti-inflammatory agents. They inhibit the synthesis of prostaglandins (PGs) and leukotrienes (LTs), but have no effect on lipoxin formation. This mechanism of combined inhibition circumvents certain limitations for selective COX-2 inhibitors and spares the gastrointestinal mucosa. Natural products, i.e. spice chemicals and herbs, offer an excellent opportunity for drug discovery. They have proven anti-inflammatory properties. However, the potential of a molecule to be a lead/ drug candidate can be much more enhanced if it has the property of inhibition in a dual mechanism. Synergistic activity is always a better option than the molecule's normal biological activity. Herein, we have explored the dual COX/5-LOX inhibition property of the three major potent phytoconsituents (curcumin, capsaicin, and gingerol) from Indian spices using in silico tools and biophysical techniques in a quest to identify their probable inhibitory role as anti-inflammatory agents. Results revealed the dual COX/5-LOX inhibitory potential of curcumin. Gingerol and capsaicin also revealed favorable results as dual COX/5-LOX inhibitors. Our results are substantiated by target similarity studies, molecular docking, molecular dynamics, energy calculations, DFT, and QSAR studies. In experimental inhibitory (in vitro) studies, curcumin exhibited the best dual inhibitory activities against COX-1/2 and 5-LOX enzymes. Capsaicin and gingerol also showed inhibitory potential against both COX and LOX enzymes. In view of the anti-inflammatory potential these spice chemicals, this research could pave the way for more scientific exploration in this area for drug discovery.

Application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer

Colon cancer is the fourth leading cause of cancer death worldwide, and its progression is accompanied by a complex array of genetic variations. CRISPR/Cas9 can identify new drug-resistant or sensitive mutations in colon cancer, and can use gene editing technology to develop new therapeutic targets and provide personalized treatments, thereby significantly improving the treatment of colon cancer patients. CRISPR/Cas9 systems are driving advances in biotechnology. RNA-directed Cas enzymes have accelerated the pace of basic research and led to clinical breakthroughs. This article reviews the rapid development of CRISPR/Cas in colon cancer, from gene editing to transcription regulation, gene knockout, genome-wide CRISPR tools, therapeutic targets, stem cell genomics, immunotherapy, metabolism-related genes and inflammatory bowel disease. In addition, the limitations and future development of CRISPR/Cas9 in colon cancer studies are reviewed. In conclusion, this article reviews the application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer.

Research on the Mechanism of Liuwei Dihuang Decoction for Osteoporosis Based on Systematic Biological Strategies

Background

Osteoporosis is an important health problem worldwide. Liuwei Dihuang Decoction (LDD) and its main ingredients may have a good clinical effect on osteoporosis. Meanwhile, its mechanism for treating osteoporosis needs to be further revealed in order to provide a basis for future drug development.

Methods

A systematic biological methodology was utilized to construct and analyze the LDD-osteoporosis network. After that, the human transcription data of LDD intervention in patients with osteoporosis and protein arrays data of LDD intervention in osteoporosis rats were collected. The human transcription data analysis, protein arrays data analysis, and molecular docking were performed to validate the findings of the prediction network (LDD-osteoporosis PPI network). Finally, animal experiments were conducted to verify the prediction results of systematic pharmacology.

Results

(1) LDD-osteoporosis PPI network shows the potential compounds, potential targets (such as ALB, IGF1, SRC, and ESR1), clusters, biological processes (such as positive regulation of calmodulin 1-monooxygenase activity, estrogen metabolism, and endothelial cell proliferation), and signaling and Reactome pathways (such as JAK-STAT signaling pathway, osteoclast differentiation, and degradation of the extracellular matrix) of LDD intervention in osteoporosis. (2) Human transcriptomics data and protein arrays data validated the findings of the LDD-osteoporosis PPI network. (3) The animal experiments showed that LDD can improve bone mineral density (BMD), increase serum estradiol (E2) and alkaline phosphatase (ALP) levels, and upregulate Wnt3a and β-catenin mRNA expression (P < 0.05). (4) Molecular docking results showed that alisol A, dioscin, loganin, oleanolic acid, pachymic acid, and ursolic acid may stably bind to JAK2, ESR1, and CTNNB1.

Conclusion

LDD may have a therapeutic effect on osteoporosis through regulating the targets (such as ALB, IGF1, SRC, and ESR1), biological processes (such as positive regulation of calmodulin 1-monooxygenase activity, estrogen metabolism, and endothelial cell proliferation), and pathways (such as JAK-STAT signaling pathway, osteoclast differentiation, and degradation of the extracellular matrix) found in this research.

Holism of Chinese herbal medicine prescriptions for inflammatory bowel disease: A review based on clinical evidence and experimental research

BACKGROUND

Inflammatory bowel disease (IBD) is a chronic nonspecific inflammatory disease that causes a heavy burden and lacks effective treatments. Chinese herbal medicine prescriptions (CHMPs), which are characterized by a synergistic usage of herbs, are widely used in the management of IBD. The molecular mechanisms of action of CHMP are still ambiguous as the canonical "one-compound-one-target" approach has difficulty describing the dynamic bioreactions among CHMP objects. It seems more flexible to define the holism of CHMP for IBD by employing high-throughput analysis. However, studies that discuss the development of CHMP in treating IBD in a holistic view are still lacking.

PURPOSE

This review appraised preclinical and clinical research to fully describe the anti-IBD capacity of CHMPs and discussed CHMPs' holistic characteristics that can contribute to better management of IBD.

METHODS & RESULTS

We screened clinical and preclinical references of CHMP being used as treatments for IBD. We discussed the complexity of IBD and the development of CHMP to present the sophistication of CHMP treatments. To describe the clinical effectiveness of CHMPs against IBD, we performed an umbrella review of CHMP-associated META analyses, in which 1174 records were filtered down to 12 references. Then, we discussed 14 kinds of CHMPs that had a long history of use and analyzed their mechanisms of action. Representative herbs were employed to provide a subordinate explanation for the whole prescription. As holism is the dominant characteristic of CHMPs, we explored applications of CHMPs for IBD with the help of omics, gut microbiome, and network pharmacology, which are potential approaches to a dynamic figure of bioactions of CHMPs.

CONCLUSION

This review is the first to discuss the potential of CHMPs to manage IBD in a holistic context and will provide inspiring explanations for CHMP applications for further product transformation and application to other diseases.

Multi-omics analysis to screen potential therapeutic biomarkers for anti-cancer compounds

Discover potential biomarkers of the response for anti-cancer therapies, including traditional Chinese medicine (TCM), is a critical but much different task in the field of cancer research. Based on accumulated data and sophisticated methods, multi-omics analysis provides a feasible strategy for the discovery of potential therapeutic biomarkers. Here, we screened the potential therapeutic biomarkers for anti-cancer compounds in TCM through multi-omics data analysis. Firstly, compounds in TCM were collected from the public databases. Then, the molecules that those compounds can intervene on cell lines were carefully filtered out from existing drug bioactivity datasets. Finally, multi-omics analysis including gene mutation analysis, differential expression gene analysis, copy number variation analysis and clinical survival analysis for pan-cancer were conducted to screen potential therapeutic biomarkers for compounds in TCM. 13 molecules of compounds in TCM namely ERBB2, MYC, FLT4, TEK, GLI1, TOP2A, PDE10A, SLC6A3, GPR55, TERT, EGFR, KCNA3 and HDAC4 are differentially expressed, high frequently mutated, obtain high copy number variation rate and also significant in survival, are considered as the potential therapeutic biomarkers.

Modular Screening Reveals Driver Induced Additive Mechanisms of Baicalin and Jasminoidin on Cerebral Ischemia Therapy

Combination therapy with increased efficacy and reduced toxicity plays a crucial role in treating complex diseases, such as stroke, but it remains an insurmountable barrier to elucidate the mechanisms of synergistic effects. Here, we present a Driver-induced Modular Screening (DiMS) strategy integrated synergistic module and driver gene identification to elucidate the additive mechanisms of Baicalin (BA) and Jasminoidin (JA) on cerebral ischemia (CI) therapy. Based on anti-ischemia genomic networks BA, JA, and their combination (BJ), we obtained 4, 3, and 9 On-modules of BA, JA, and BJ by modular similarity analysis. Compared with the monotherapy groups, four additive modules (Add-module, BJ_Mod-4, 7, 9, and 13), 15 driver genes of BJ were identified by modular similarity and network control methods, and seven driver proteins (PAQR8, RhoA, EMC10, GGA2, VIPR1, FAM120A, and SEMA3F) were validated by animal experiments. The functional analysis found neuroprotective roles of the Add-modules and driver genes, such as the Neurotrophin signaling pathway and FoxO signaling pathway, which may reflect the additive mechanisms of BJ. Moreover, such a DiMS paradigm provides a new angle to explore the synergistic mechanisms of combination therapy and screen multi-targeted drugs for complex diseases.

The network pharmacology integrated with pharmacokinetics to clarify the pharmacological mechanism of absorbed components from Viticis fructus extract

ETHNOPHARMACOLOGICAL RELEVANCE

Viticis fructus (VF) has been widely used in alleviating the swelling and pain, owning to its pharmacologically active components including agnuside, 10-O-vanilloylaucubin, luteolin and casticin.

AIM OF THE STUDY

The pharmacokinetic profiles of the absorbed components from aqueous and ethanolic extracts of VF in rat plasma were performed, and explored the molecular mechanisms of absorbed components via network pharmacology.

MATERIALS AND METHODS

Ultra-performance liquid chromatography-tandem mass spectroscopy (UHPLC-MS/MS) was employed to identify the absorbed components from rat plasma. Liquid-liquid extraction with ethyl acetate was used to purify the plasma samples. Plasma pharmacokinetics parameters of the components absorbed were analyzed after oral administration of both extracts. Network pharmacology was used to predict the biological functions and potential signaling pathways of VF. The anti-cancer effects of VF extract and absorbed components have been confirmed by in vitro experiments.

RESULTS

The method was very sensitive with lower limit of quantification (LLOQ) of 1.0, 2.5, 0.2 and 0.5 ng/mL for agnuside, 10-O-vanilloylaucubin, luteolin and casticin, respectively. With the exception of 10-O-vanilloylaucubin which was not detected in the ethanolic extract of VF, all other components were detected in both extracts in plasma. The pharmacokinetic parameters of the four components from rat plasma were significantly different between the two extracts. According to the results of network pharmacology, the absorption components of VF are enriched in 32 key pathways, and 15 pathways are related to cancer. Ultimately, the anti-cancer effects, as well as the signaling pathways of VF ethanolic extract and absorbed components were verified by in vitro experiments.

CONCLUSION

The optimized, sensitive and validated UHPLC-MS/MS method was successfully applied for the plasma pharmacokinetics comparison analysis of the two VF extracts. The combination of network pharmacology and pharmacokinetics provides a useful method to elucidate the biological effects and molecular mechanism of the absorbed components of VF.

Integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action

An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the primary drug targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary, pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is a first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

FordNet: Recommending traditional Chinese medicine formula via deep neural network integrating phenotype and molecule

Traditional Chinese medicine (TCM) formula is widely used for thousands of years in clinical practice. With the development of artificial intelligence, deep learning models may help doctors prescribe reasonable formulas. Meanwhile, current studies of formula recommendation only focus on the observable clinical symptoms and lack of molecular information. Here, inspired by the theory of TCM network pharmacology, we propose an intelligent formula recommendation system based on deep learning (FordNet), fusing the information of phenotype and molecule. We collected more than 20,000 electronic health records from TCM Master Li Jiren's experience from 2013 to March 2020. In the FordNet system, the feature of diagnosis description is extracted by convolution neural network and the feature of TCM formula is extracted by network embedding, which fusing the molecular information. A hierarchical sampling strategy for data augmentation is designed to effectively learn training samples. Based on the expanded samples, a deep neural network based quantitative optimization model is developed for TCM formula recommendation. FordNet performs significantly better than baseline methods (hit ratio of top 10 improved by 46.9% compared with the best baseline random forest method). Moreover, the molecular information helps FordNet improve 17.3% hit ratio compared with the model using only macro information. Clinical evaluation shows that FordNet can well learn the effective experience of TCM Master and obtain excellent recommendation results. Our study, for the first time, proposes an intelligent recommendation system for TCM formula integrating phenotype and molecule information, which has potential to improve clinical diagnosis and treatment, and promote the shift of TCM research pattern from "experience based, macro" to "data based, macro-micro combined" as well as the development of TCM network pharmacology.

Exploring the Mechanism of Berberine Intervention in Ulcerative Colitis from the Perspective of Inflammation and Immunity Based on Systemic Pharmacology

Background

Ulcerative colitis (UC) is a chronic nonspecific inflammatory disease of the colon and rectum. Recent studies found that berberine had effects on inflammatory diseases and immune diseases.

Methods

The PharmMapper database was used to predict the berberine potential target and GeneCards database and OMIM database were utilized to collect UC genes. The Cytoscape software was used to construct and analyze the networks and DAVID was utilized to perform enrichment analysis. Then, animal experiments were performed to validate the prediction results. The experimental rats were randomly divided into normal group (control group), model group, and berberine group. The general condition, body weight, gross morphology of colon tissue, and colonic mucosal damage index (CMDI) score were observed. The pathological changes of colon tissue were observed by H&E staining. The levels of serum interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and IL-4 were detected by ELISA. The expressions of IL-1β, TNF-α, and IL-4 protein in colon tissue were detected by immunohistochemistry.

Results

A total of 211 Berberine's potential targets and 210 UC genes were obtained. The enrichment analysis showed that berberine may regulate inflammation, inflammatory cytokines, and their mediated inflammation signal pathways such as inflammatory bowel disease (IBD), rheumatoid arthritis, cytokine-cytokine receptor interaction, TNF, T cell receptor, Toll-like receptor, and JAK/STAT signaling pathway. Compared with the model group, the body mass of rats in the berberine group was significantly increased (P < 0.05); the general morphology and pathological changes of colon tissue were significantly improved; CMDI score, serum and colon tissue IL-1β, TNF-α content, and protein expression were decreased significantly (P < 0.05); and IL-4 content and protein expression increased significantly (P < 0.05).

Conclusion

Berberine can interfere with UC through related biological processes and signal pathways related to inflammation and immunity. In-depth exploration of the mechanism of berberine in the treatment of UC will provide a basis for clinical application.

CRISPR screens in plants: approaches, guidelines, and future prospects

Clustered regularly interspaced short palindromic repeat (CRISPR)-associated systems have revolutionized genome engineering by facilitating a wide range of targeted DNA perturbations. These systems have resulted in the development of powerful new screens to test gene functions at the genomic scale. While there is tremendous potential to map and interrogate gene regulatory networks at unprecedented speed and scale using CRISPR screens, their implementation in plants remains in its infancy. Here we discuss the general concepts, tools, and workflows for establishing CRISPR screens in plants and analyze the handful of recent reports describing the use of this strategy to generate mutant knockout collections or to diversify DNA sequences. In addition, we provide insight into how to design CRISPR knockout screens in plants given the current challenges and limitations and examine multiple design options. Finally, we discuss the unique multiplexing capabilities of CRISPR screens to investigate redundant gene functions in highly duplicated plant genomes. Combinatorial mutant screens have the potential to routinely generate higher-order mutant collections and facilitate the characterization of gene networks. By integrating this approach with the numerous genomic profiles that have been generated over the past two decades, the implementation of CRISPR screens offers new opportunities to analyze plant genomes at deeper resolution and will lead to great advances in functional and synthetic biology.

Combinatorial CRISPR screen identifies fitness effects of gene paralogues

Genetic redundancy has evolved as a way for human cells to survive the loss of genes that are single copy and essential in other organisms, but also allows tumours to survive despite having highly rearranged genomes. In this study we CRISPR screen 1191 gene pairs, including paralogues and known and predicted synthetic lethal interactions to identify 105 gene combinations whose co-disruption results in a loss of cellular fitness. 27 pairs influence fitness across multiple cell lines including the paralogues FAM50A/FAM50B, two genes of unknown function. Silencing of FAM50B occurs across a range of tumour types and in this context disruption of FAM50A reduces cellular fitness whilst promoting micronucleus formation and extensive perturbation of transcriptional programmes. Our studies reveal the fitness effects of FAM50A/FAM50B in cancer cells.

Study of the active ingredients and mechanism of Sparganii rhizoma in gastric cancer based on HPLC-Q-TOF-MS/MS and network pharmacology

Sparganii rhizoma (SL) has potential therapeutic effects on gastric cancer (GC), but its main active ingredients and possible anticancer mechanism are still unclear. In this study, we used HPLC-Q-TOF–MS/MS to comprehensively analyse the chemical components of the aqueous extract of SL. On this basis, a network pharmacology method incorporating target prediction, gene function annotation, and molecular docking was performed to analyse the identified compounds, thereby determining the main active ingredients and hub genes of SL in the treatment of GC. Finally, the mRNA and protein expression levels of the hub genes of GC patients were further analysed by the Oncomine, GEPIA, and HPA databases. A total of 41 compounds were identified from the aqueous extract of SL. Through network analysis, we identified seven main active ingredients and ten hub genes: acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, caffeic acid, adenine nucleoside, azelaic acid and PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA. The results indicated that SL might play a role in GC treatment by controlling the PI3K-Akt and other signalling pathways to regulate biological processes such as proliferation, apoptosis, migration, and angiogenesis in tumour cells. In conclusion, this study used HPLC-Q-TOF–MS/MS combined with a network pharmacology approach to provide an essential reference for identifying the chemical components of SL and its mechanism of action in the treatment of GC.

Effects of combined treatment with Indomethacin and Juglone on AOM/DSS induced colon carcinogenesis in Balb/c mice: Roles of inflammation and apoptosis

AIM

Indomethacin [IND] is reported to treat colon cancer. However, continuous exposure to IND causes gastric ulceration, an adverse side effect in humans. This study implies the therapeutic effect of IND and juglone [JUG] against colon carcinogenesis, without gastric ulceration - an adverse side effect of IND.

MATERIALS AND METHODS

Adult male Balb/C mice were divided into six groups randomly: control, AOM/DSS-induced, IND-treated, JUG-treated, IND + JUG-treated and drug-control. Levels of serum markers, haematoxylin & eosin staining to observe tissue architecture, toluidine blue staining to detect mast cells expression, Masson's trichrome and sirius-red staining were used to detect the collagen deposition. RT-PCR and western blot analysis were carried out to detect inflammation and apoptosis.

KEY FINDINGS

IND + JUG effectively decreased the levels of serum markers: CEA, AFP, LDH, AST and ALT. Although, IND restored colonic architecture by regulating the accumulation of mast cell and collagen content, it causes gastric ulceration. To address this adverse effect of IND, JUG was given along with IND and was shown to alleviate IND-induced gastric ulceration. AOM/DSS induced animals showed increased expression of inflammatory molecules - TNFα, NFκB and Cox-2, apoptosis regulator - Bcl-2 and decreased expression of pro-apoptotic molecules - Bad, Bax and caspase3; whereas, IND and JUG treated groups showed decreased inflammatory expression with increased expression of pro-apoptotic molecules.

SIGNIFICANCE

IND and JUG reduce the inflammatory activity and induce apoptotic cell death, while JUG effectively prevents IND induced gastric ulceration. These findings establish that a combination of IND + JUG may serve as a promising treatment regimen for colon cancer.

TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches

Traditional Chinese medicine (TCM) is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of diseases. The holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and system. TCM gave birth to a new method featuring holistic and systematic "network target", a core theory and method of network pharmacology. TCM is also an important research object of network pharmacology. TCM network pharmacology, which aims to understand the network-based biological basis of complex diseases, TCM syndromes and herb treatments, plays a critical role in the origin and development process of network pharmacology. This review introduces new progresses of TCM network pharmacology in recent years, including predicting herb targets, understanding biological foundation of diseases and syndromes, network regulation mechanisms of herbal formulae, and identifying disease and syndrome biomarkers based on biological network. These studies show a trend of combining computational, experimental and clinical approaches, which is a promising direction of TCM network pharmacology research in the future. Considering that TCM network pharmacology is still a young research field, it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.

Network-based approach highlighting interplay among anti-hypertensives: target coding-genes: diseases

Elucidating the relation between the medicines: targets, targets: diseases and diseases: diseases are of fundamental significance as-is for societal benefit. Hypertension is one of the dangerous health conditions prevalent in society, is a risk factor for several other diseases if left untreated and anti-hypertensives (AHs) are the approved drugs to treat it. The goal of the study is to decipher the connection between hypertension with other health conditions, however, is challenging due to the large interactome. To fulfill the aim, the strategy involves prior clustering of the AHs into groups as per our previous method, followed by the analyzing functional association of the target coding-genes (tc-genes) and health conditions for each group. Following our recently published work where the AHs are clustered into six groups such that molecules having similar patterns come together, here, the distribution of molecular functions and the cellular components adopted by the tc-genes of each group are analyzed. The analyses indicate that kidney, heart, brain or lung related ailments are commonly associated with the tc-genes. The association of selective tc-genes to health conditions suggests a preference for certain health conditions despite many possibilities. Analyses of experimentally validated drug–drug combinations indicate the trend in successful AHs combinations. Clinically validated combinations bind different targets. Our study provides a promising methodology in a network-based approach that considers the influence of structural diversity of AHs to the functional perspective of tc-genes concerning the health conditions. The method could be extended to explore disease–disease relationships.

Network Pharmacology Analysis to Uncover the Potential Mechanisms of Lycium barbarum on Colorectal Cancer

BACKGROUND

Studies have shown that extracts from Lycium barbarum exerted protective effects against colorectal cancer (CRC) cells. We used the network pharmacology method to determine the effects of L. barbarum on CRC and to predict core targets, biological functions, pathways, and mechanisms of action.

METHOD

We obtained the active compounds and their targets in L. barbarum via use of the Traditional Chinese Medicine System Pharmacology Database (TCMSP), gathered the CRC targets from Malacards, TTD, GeneCards, and DisGeNET, and chosen the overlapped targets as the candidate targets. After protein-protein interaction (PPI) network analysis, 20 with the highest node degree were selected as the core targets, and their enrichment and pathways were analyzed. Furthermore, we employed iGEMDOCK to validate the compound-target relation.

RESULT

Eventually, 103 overlapped targets were chosen as the candidate targets. Targets with the top 20 highest node degree were selected as the core targets. Gene Ontology (GO) enrichment analysis indicated that the core targets were enriched in cell proliferation regulation, extracellular space, cytokine receptor binding, and so on. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis proved that the core targets were significantly enriched in bladder cancer, pathways in cancer. The docking results demonstrated that beta-sitosterol, glycitein, and quercetin had good binding activity to CRC putative targets.

CONCLUSION

Our work successfully predicted the functioning ingredients and potential targets of L. barbarum in CRC and illustrated the potential pathways and mechanisms comprehensively. Nevertheless, these results still call for in vitro and in vivo experiments to validate.

Artificial Intelligence-Based Multiclass Classification of Benign or Malignant Mucosal Lesions of the Stomach

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. It takes some time from chronic gastritis to develop in GC. Early detection of GC will help patients obtain timely treatment. Understanding disease evolution is crucial for the prevention and treatment of GC. Here, we present a convolutional neural network (CNN)-based system to detect abnormalities in the gastric mucosa. We identified normal mucosa, chronic gastritis, and intestinal-type GC: this is the most common route of gastric carcinogenesis. We integrated digitalizing histopathology of whole-slide images (WSIs), stain normalization, a deep CNN, and a random forest classifier. The staining variability of WSIs was reduced significantly through stain normalization, and saved the cost and time of preparing new slides. Stain normalization improved the effect of the CNN model. The accuracy rate at the patch-level reached 98.4%, and 94.5% for discriminating normal → chronic gastritis → GC. The accuracy rate at the WSIs-level for discriminating normal tissue and cancerous tissue reached 96.0%, which is a state-of-the-art result. Survival analyses indicated that the features extracted from the CNN exerted a significant impact on predicting the survival of cancer patients. Our CNN model disclosed significant potential for adjuvant diagnosis of gastric diseases, especially GC, and usefulness for predicting the prognosis.

Genome-wide CRISPR screens for the identification of therapeutic targets for cancer treatment

INTRODUCTION

Exploring the function of every gene is a challenging task. There is a paradigm shift of RNA interference with the introduction of clustered regularly interspaced short palindromic repeat (CRISPR)-based genome-wide screening. CRISPR-based screening can detect the loss-of-function and gain-of-function targets. Many DNA-binding proteins are engineered as effective tools for modulating gene expression and for investigating therapeutic targets for a spectrum of diseases. Among them, CRISPR-Cas9 has received extensive attention with its potential for screening cancer treatment targets.

AREAS COVERED

This article reviews CRISPR toolkit and its applications in screening cancer therapeutic targets, especially genome-wide screens using different CRISPR-Cas9 systems. We compare and summarize the characteristics of CRISPR systems, which would be helpful for understanding and optimizing current CRISPR toolkits, as well as reflecting on the potential future development and clinical applications of CRISPR screens.

EXPERT OPINION

The application of CRISPR-based therapeutic target screening is broadly used in cancer drug development. Its application in cancer immunotherapy and precision oncology is blooming. Nevertheless, more effective methods of Cas protein delivery and the development of more accurate and efficient genome-editing tools are needed.

Integrated bioinformatics analysis to decipher molecular mechanism of compound Kushen injection for esophageal cancer by combining WGCNA with network pharmacology

Compound Kushen injection (CKI), a medicine in widespread clinical use in China, has proven therapeutic effects on cancer. However, few molecular mechanism analyses have been carried out. To address this problem, bioinformatics approaches combining weighted gene co-expression network analysis with network pharmacology methods were undertaken to elucidate the underlying molecular mechanisms of CKI in the treatment of esophageal cancer (ESCA). First, the key gene modules related to the clinical traits of ESCA were analysed by WCGNA. Based on the results, the hub genes related to CKI treatment for ESCA were explored through network pharmacology. Molecular docking simulation was performed to recognize the binding activity of hub genes with CKI compounds. The results showed that the potential hub targets, including EGFR, ErbB2, CCND1 and IGF1R, are therapeutic targets of CKI for the treatment of ESCA. Moreover, these targets were significantly enriched in many pathways related to cancer and signalling pathways, such as the PI3K-Akt signalling pathway and ErbB signalling pathway. In conclusion, this research partially highlighted the molecular mechanism of CKI in the treatment of ESCA, offering great potential in the identification of the effective compounds in CKI and biomarkers for ESCA treatment.

Database of adverse events associated with drugs and drug combinations

Due to the aging world population and increasing trend in clinical practice to treat patients with multiple drugs, adverse events (AEs) are becoming a major challenge in drug discovery and public health. In particular, identifying AEs caused by drug combinations remains a challenging task. Clinical trials typically focus on individual drugs rather than drug combinations and animal models are unreliable. An added difficulty is the combinatorial explosion in the number of possible combinations that can be made using the increasingly large set of FDA approved chemicals. We present a statistical and computational technique for identifying AEs caused by two-drug combinations. Taking advantage of the large and increasing data deposited in FDA’s postmarketing reports, we demonstrate that the task of predicting AEs for 2-drug combinations is amenable to the Likelihood Ratio Test (LRT). Our pAERS database constructed with LRT contains almost 77 thousand associations between pairs of drugs and corresponding AEs caused solely by drug-drug interactions (DDIs). The DDIs stored in pAERS complement the existing data sets. Due to our stringent statistical test, we expect many of the associations in pAERS to be unrecorded or poorly documented in the literature.

Exploring the Pharmacological Mechanism of Quercetin-Resveratrol Combination for Polycystic Ovary Syndrome: A Systematic Pharmacological Strategy-Based Research

Resveratrol and quercetin have effects on polycystic ovary syndrome (PCOS). Hence, resveratrol combined with quercetin may have better effects on it. However, because of the limitations in animal and human experiments, the pharmacological and molecular mechanism of quercetin-resveratrol combination (QRC) remains to be clarified. In this research, a systematic pharmacological approach comprising multiple compound target collection, multiple potential target prediction, and network analysis was used for comparing the characteristic of resveratrol, quercetin and QRC, and exploring the mechanism of QRC. After that, four networks were constructed and analyzed: (1) compound-compound target network; (2) compound-potential target network; (3) QRC-PCOS PPI network; (4) QRC-PCOS-other human proteins (protein-protein interaction) PPI network. Through GO and pathway enrichment analysis, it can be found that three compounds focus on different biological processes and pathways; and it seems that QRC combines the characteristics of resveratrol and quercetin. The in-depth study of QRC further showed  more PCOS-related biological processes and pathways. Hence, this research not only offers clues to the researcher who is interested in comparing the differences among resveratrol, quercetin and QRC, but also provides hints for the researcher who wants to explore QRC's various synergies and its pharmacological and molecular mechanism.

Chronic Stress, Inflammation, and Colon Cancer: A CRH System-Driven Molecular Crosstalk

Chronic stress is thought to be involved in the occurrence and progression of multiple diseases, via mechanisms that still remain largely unknown. Interestingly, key regulators of the stress response, such as members of the corticotropin-releasing-hormone (CRH) family of neuropeptides and receptors, are now known to be implicated in the regulation of chronic inflammation, one of the predisposing factors for oncogenesis and disease progression. However, an interrelationship between stress, inflammation, and malignancy, at least at the molecular level, still remains unclear. Here, we attempt to summarize the current knowledge that supports the inseparable link between chronic stress, inflammation, and colorectal cancer (CRC), by modulation of a cascade of molecular signaling pathways, which are under the regulation of CRH-family members expressed in the brain and periphery. The understanding of the molecular basis of the link among these processes may provide a step forward towards personalized medicine in terms of CRC diagnosis, prognosis and therapeutic targeting.

Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity

Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and similarity integration parts of NDD have been proposed in previous studies of other problems. Our novelty is to combine these parts with new neural network architecture and apply these approaches in the context of DDI prediction. We compared NDD with six machine learning classifiers and six state-of-the-art graph-based methods on three benchmark datasets. NDD achieved superior performance in cross-validation with AUPR ranging from 0.830 to 0.947, AUC from 0.954 to 0.994 and F-measure from 0.772 to 0.902. Moreover, cumulative evidence in case studies on numerous drug pairs, further confirm the ability of NDD to predict unknown DDIs. The evaluations corroborate that NDD is an efficient method for predicting unknown DDIs. The data and implementation of NDD are available at https://github.com/nrohani/NDD.