Multiscale Modeling of Inflammation-Induced Tumorigenesis Reveals Competing Oncogenic and Oncoprotective Roles for Inflammation

A novel cytogenetic method to image chromatin interactions at subkilobase resolution: Tn5 transposase-based fluorescence in situ hybridization

There is an increasing interest in understanding how three-dimensional organization of the genome is regulated. Different strategies have been used to identify genome-wide chromatin interactions. However, owing to current limitations in resolving genomic contacts, visualization and validation of these genomic loci at subkilobase resolution remain unsolved to date. Here, we describe Tn5 transposase-based fluorescence in situ hybridization (Tn5-FISH), a polymerase chain reaction-based, cost-effective imaging method, which can colocalize the genomic loci at subkilobase resolution, dissect genome architecture, and verify chromatin interactions detected by chromatin configuration capture-derived methods. To validate this method, short-range interactions in the keratin-encoding gene (KRT) locus in the topologically associated domain were imaged by triple-color Tn5-FISH, indicating that Tn5-FISH is very useful to verify short-range chromatin interactions inside the contact domain and TAD. Therefore, Tn5-FISH can be a powerful molecular tool for clinical detection of cytogenetic changes in numerous genetic diseases such as cancers.

Network Pharmacology-Based Study on the Mechanism of Gegen Qinlian Decoction against Colorectal Cancer

Purpose

Gegen Qinlian decoction (GQD) has been used to treat gastrointestinal diseases, such as diarrhea and ulcerative colitis (UC). A recent study demonstrated that GQD enhanced the effect of PD-1 blockade in colorectal cancer (CRC). This study used network pharmacology analysis to investigate the mechanisms of GQD as a potential therapeutic approach against CRC.

Materials and Methods

Bioactive chemical ingredients (BCIs) of GQD were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CRC-specific genes were obtained using the gene expression profile GSE110224 from the Gene Expression Omnibus (GEO) database. Target genes related to BCIs of GQD were then screened out. The GQD-CRC ingredient-target pharmacology network was constructed and visualized using Cytoscape software. A protein-protein interaction (PPI) network was subsequently constructed and analyzed with BisoGenet and CytoNCA plug-in in Cytoscape. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were then performed using the R package of clusterProfiler.

Results

One hundred and eighteen BCIs were determined to be effective on CRC, including quercetin, wogonin, and baicalein. Twenty corresponding target genes were screened out including PTGS2, CCNB1, and SPP1. Among these genes, CCNB1 and SPP1 were identified as crucial to the PPI network. A total of 212 GO terms and 6 KEGG pathways were enriched for target genes. Functional analysis indicated that these targets were closely related to pathophysiological processes and pathways such as biosynthetic and metabolic processes of prostaglandins and prostanoids, cytokine and chemokine activities, and the IL-17, TNF, Toll-like receptor, and nuclear factor-kappa B (NF-κB) signaling pathways.

Conclusion

The study elucidated the “multiingredient, multitarget, and multipathway” mechanisms of GQD against CRC from a systemic perspective, indicating GQD to be a candidate therapy for CRC treatment.

Quantitative and network pharmacology: A case study of rhein alleviating pathological progress of renal interstitial fibrosis

ETHNOPHARMACOLOGICAL RELEVANCE

The current network pharmacology model focuses mainly on static and qualitative characterisation between drugs and targets or molecular pathway networks, but it does not reflect the multi-scale, dynamic and quantitative process of drug action.

AIM OF THE STUDY

In this study, we developed a new model known as quantitative and network pharmacology (QNP) to characterise the dynamic and quantitative interventions of drugs within a multi-scale biological network.

MATERIALS AND METHODS

Firstly, we used a systems biology method to construct a molecule-cell dynamic network model to simulate the pathological processes of diseases. Secondly, according to the principles of enzymatic kinetics, we generated a multi-scale drug intervention model to simulate the intervention of drugs in multi-scale networks at different concentrations and pathological stages. Finally, we took rhein treatment of renal interstitial fibrosis (RIF) as an example to illustrate the QNP model.

RESULTS

We successfully constructed the a QNP model that includes both a multi-scale dynamic network disease model and drug intervention model. The QNP model accurately simulated the pathological process of RIF, and the simulation results were validated by a series of cell and animal experiments. Meanwhile, the QNP model demonstrated that rhein can delay the pathological process at the studied concentrations of 5 nM, 10 nM, and 20 nM, and can also exert a better therapeutic effect on fibrosis before the proliferation stage of RIF. Furthermore, through uncertainty and sensitivity analysis, we identified that FAK and Smad3 may be potential targets for RIF.

CONCLUSION

Our QNP model provides a molecular-cellular understanding of the pathological mechanisms of RIF, serving as a new approach and strategy for the construction of dynamic multi-scale network model of diseases and drug intervention.

Chinese herbal medicine promote tissue differentiation in colorectal cancer by activating HSD11B2

BACKGROUND

Colorectal cancer is a common malignant tumor of the digestive tract. In recent years, the incidence rate has increased year by year and is showing a younger trend. The application of Chinese herbal medicine (CHM) is one of the important methods for the treatment of colorectal cancer. CHM refers to the main therapeutic drugs based on traditional Chinese medicine (TCM), which is still valued. Many effective anticancer small-molecule compounds are derived from CHMs, and their effective anticancer ingredients and targets must be clarified and to further understand the molecular mechanisms by which CHM affects cancer.

METHODS

We analyzed the ingredients in CHM that were found to be effective against colorectal cancer and constructed an interaction network among these ingredients and the target protein. By analyzing the number of connections in the network and their type of interaction, we identified the key target protein Corticosteroid 11-beta-dehydrogenase isozyme 2, the enzyme encoded by HSD11B2. Analyses of HSD11B2 expression, survival curve, and co-expressed genes helped clarify the correlation between HSD11B2 and colorectal cancer as well as its underlying molecular mechanism.

RESULTS

We determined that the anticancer ingredients contained in Sanguisorba officinalis, Patrinia scabiosaefolia, and Smilax china had more connections to the target proteins found in colorectal cancer. In the interaction network, eight small-molecule compounds had an activating effect on HSD11B2. The expression of the HSD11B2 was markedly decreased in colorectal cancer tissues and was positively correlated with the overall survival time of patients. In addition, co-expression analyses showed a close relationship between HSD11B2 and tissue-specific genes in colorectal tissues. The expression levels of HSD11B2 in well-, moderately, and poorly differentiated tissues progressively decreased.

CONCLUSION

The HSD11B2 protein was a key CHM target for treating colorectal cancer. The key role of CHM may lie in activating HSD11B2 and further promoting tissue differentiation in colorectal cancer.

CBX3 Promotes Gastric Cancer Progression and Affects Factors Related to Immunotherapeutic Responses

Background

Chromobox 3 (CBX3) is a member of the chromobox family proteins, which plays a critical role in tumor progression, but the exact function of CBX3 in gastric cancer remains unknown. The current research mainly investigates the underlying mechanisms and clinical value of CBX3 in gastric cancer.

Methods

Gene expression cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to assess the effect of CBX3 in gastric cancer. CBX3 expression was further determined by immunohistochemistry (IHC). The function of CBX3 on proliferation, migration and the cell cycle was explored via colony-forming, cell cycle and transwell assays, respectively. Moreover, RNA sequencing (RNA-seq) in AGS cells and two cohorts was utilized to explore the specific mechanism of CBX3.

Results

CBX3 expression was upregulated in human gastric cancer tissues and the expression level was closely associated with adverse signs. Knockdown of CBX3 in gastric cancer cells significantly inhibited the malignant phenotype. In addition, RNA-seq analysis revealed that CBX3 regulates genes related to the cell cycle, mismatch repair and immune-related pathways. Furthermore, the expression of CBX3 was significantly and inversely related to the abundance of tumor-infiltrating lymphocytes (TILs), PDCD1 and PDCD1LG2 expression and immunotherapy responses. Moreover, CBX3 influences the effectiveness of chemotherapy, thereby impacting the prognosis of gastric cancer patients.

Conclusion

CBX3 contributes to gastric cancer progression and is associated with chemotherapy and immunotherapy response. CBX3 may serve as a new diagnostic biomarker and potential target for immunotherapy and chemotherapy in gastric cancer.

The Variation of Peripheral Inflammatory Markers in Vocal Leukoplakia before and after Recurrence and Canceration

Background

This retrospective study aims at comparing the variation of peripheral inflammatory markers in recurrent and cancerous vocal fold leukoplakia (VFL) and at exploring the potential connection with pathological outcomes.

Methods

The patients undergoing carbon dioxide laser surgery with postoperative pathological diagnosis of recurrent vocal fold leukoplakia in the last 5 years were included. The clinical data were collected, and neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocytes-to-lymphocyte ratio (MLR) before and after recurrence and canceration were calculated. Related comparison with two-grade pathological classification was made to evaluate their potential connection with postsurgical histopathology and clinical events.

Results

The data of 193 patients were engaged into research, as 111 in the recurrence group (Group A) and 82 in canceration group (Group B). The NLR, PLR, and MLR were significantly increased in canceration event compared to the first (P = 0.009, 0.004, 0.007, respectively) and penultimate (P = 0.013, 0.041, 0.006, respectively) time when the previous pathologies were leukoplakia. When redividing the Group A according to the two-grade pathological classification, the high-risk groups showed statistically higher NLR and PLR values than low-risk groups in the subgroups with grade changing (P = 0.016, 0.005, 0.007, 0.005, respectively) and subgroups without grade changing (P = 0.020, 0.027, 0.030, 0.029, respectively).

Conclusions

NLR, PLR, and MLR are reliable biomarkers in the circulation system which show significantly interrelation with the pathological progression of vocal fold leukoplakia. Presurgical evaluation of NLR, PLR, and MLR may have potential values to indicate the following treatment in clinical practice.

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.

A large-scale CRISPR screen and identification of essential genes in cellular senescence bypass

Cellular senescence is an important mechanism of autonomous tumor suppression, while its consequence such as the senescence-associated secretory phenotype (SASP) may drive tumorigenesis and age-related diseases. Therefore, controlling the cell fate optimally when encountering senescence stress is helpful for anti-cancer or anti-aging treatments. To identify genes essential for senescence establishment or maintenance, we carried out a CRISPR-based screen with a deliberately designed single-guide RNA (sgRNA) library. The library comprised of about 12,000 kinds of sgRNAs targeting 1378 senescence-associated genes selected by integrating the information of literature mining, protein-protein interaction network, and differential gene expression. We successfully detected a dozen gene deficiencies potentially causing senescence bypass, and their phenotypes were further validated with a high true positive rate. RNA-seq analysis showed distinct transcriptome patterns of these bypass cells. Interestingly, in the bypass cells, the expression of SASP genes was maintained or elevated with CHEK2, HAS1, or MDK deficiency; but neutralized with MTOR, CRISPLD2, or MORF4L1 deficiency. Pathways of some age-related neurodegenerative disorders were also downregulated with MTOR, CRISPLD2, or MORF4L1 deficiency. The results demonstrated that disturbing these genes could lead to distinct cell fates as a consequence of senescence bypass, suggesting that they may play essential roles in cellular senescence.

Dissecting expression profiles of gastric precancerous lesions and early gastric cancer to explore crucial molecules in intestinal-type gastric cancer tumorigenesis

Intestinal‐type gastric cancer (IGC) has a clear and multistep histological evolution. No studies have comprehensively explored gastric tumorigenesis from inflammation through low‐grade intraepithelial neoplasia (LGIN) and high‐grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC). We sought to investigate the characteristics participating in IGC tumorigenesis and identify related prognostic information within the process. RNA expression profiles of 94 gastroscopic biopsies from 47 patients, including gastric precancerous lesions (GPL: LGIN and HGIN), EGC, and paired controls, were detected by Agilent Microarray. During IGC tumorigenesis from LGIN through HGIN to EGC, the number of activity‐changed tumor hallmarks increased. LGIN and HGIN had similar expression profiles when compared to EGC. We observed an increase in the stemness of gastric epithelial cells in LGIN, HGIN, and EGC, and we found 27 consistent genes that might contribute to dedifferentiation, including five driver genes. Remarkably, we perceived that the immune microenvironment was more active in EGC than in GPL, especially in the infiltration of lymphocytes and macrophages. We identified a five‐gene signature from the gastric tumorigenesis process that could independently predict the overall survival and disease‐free survival of GC patients (log‐rank test: p < 0.0001), and the robustness was verified in an independent cohort (n > 300) and by comparing with two established prognostic signatures in GC. In conclusion, during IGC tumorigenesis, cancer‐like changes occur in LGIN and accumulate in HGIN and EGC. The immune microenvironment is more active in EGC than in LGIN and HGIN. The identified signature from the tumorigenesis process has robust prognostic significance for GC patients. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

A general mathematical framework for understanding the behavior of heterogeneous stem cell regeneration

Stem cell heterogeneity is essential for homeostasis in tissue development. This paper establishes a general mathematical framework to model the dynamics of stem cell regeneration with cell heterogeneity and random transitions of epigenetic states. The framework generalizes the classical G0 cell cycle model and incorporates the epigenetic states of individual cells represented by a continuous multidimensional variable. In the model, the kinetic rates of cell behaviors, including proliferation, differentiation, and apoptosis, are dependent on their epigenetic states, and the random transitions of epigenetic states between cell cycles are represented by an inheritance probability function that describes the conditional probability of cell state changes. Moreover, the model can be extended to include genotypic changes and describe the process of gene mutation-induced tumor development. The proposed mathematical framework provides a generalized formula that helps us to understand various dynamic processes of stem cell regeneration, including tissue development, degeneration, and abnormal growth.

Anti-Inflammatory Drugs as Anticancer Agents

Inflammation is strictly associated with cancer and plays a key role in tumor development and progression. Several epidemiological studies have demonstrated that inflammation can predispose to tumors, therefore targeting inflammation and the molecules involved in the inflammatory process could represent a good strategy for cancer prevention and therapy. In the past, several clinical studies have demonstrated that many anti-inflammatory agents, including non-steroidal anti-inflammatory drugs (NSAIDs), are able to interfere with the tumor microenvironment by reducing cell migration and increasing apoptosis and chemo-sensitivity. This review focuses on the link between inflammation and cancer by describing the anti-inflammatory agents used in cancer therapy, and their mechanisms of action, emphasizing the use of novel anti-inflammatory agents with significant anticancer activity.

Mechanistic evaluation of gastro-protective effects of KangFuXinYe on indomethacin-induced gastric damage in rats

KangFuXinYe (KFX), the ethanol extract of the dried whole body of Periplaneta americana, is a well-known important Chinese medicine preparation that has been used to treat digestive diseases such as gastric ulcers for many years in China. However, its therapeutic effect and mechanism are not yet well understood. Thus, the aim of this study was to investigate the gastro-protective effects of KangFuXinYe (KFX) in indomethacin-induced gastric damage. Rats were randomly divided into six groups as follows: control, treated with indomethacin (35 mg·kg^-1), different dosages of KFX (2.57, 5.14 and 10.28 mL·kg^-1, respectively) plus indomethacin, and sucralfate (1.71 mL·kg^-1) plus indomethacin. After treatment, rat serum, stomach and gastric homogenates were collected for biochemical tests and examination of histopathology firstly. Rat serum was further used for metabolomics analysis to research possible mechanisms. Our results showed that KFX treatment alleviated indomethacin-induced histopathologic damage in rat gastric mucosa. Meanwhile, its treatment significantly increased cyclooxygenase-1 (COX-1), prostaglandin E₂ (PGE₂) and epidermal growth factor (EGF) levels in rat serum and gastric mucosa. Moreover, KFX decreased cyclooxygenase-2 (COX-2) and interleukin-6 (IL-6) levels. Nine metabolites were identified which intensities significantly changed in gastric damage rats, including 5-hydroxyindoleacetic acid, indoxylsulfuric acid, indolelactic acid, 4-hydroxyindole, pantothenic acid, isobutyryl carnitine, 3-methyl-2-oxovaleric acid, sphingosine 1-phosphate, and indometacin. These metabolic deviations came to closer to normal levels after KFX intervention. The results indicate that KFX (10.28 mL·kg^-1) exerts protective effects on indomethacin-induced gastric damage by possible mechanisms of action (regulating tryptophan metabolism, protecting the mitochondria, and adjusting lipid metabolism, and reducing excessive indomethacin).

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.

Methanolic extract of Potentilla fulgens root and its ethyl-acetate fraction delays the process of carcinogenesis in mice

People of north-eastern states of India consume raw areca-nut (RAN) and lime which could lead to oral, esophageal and gastric cancers. However, the incidence of these cancers are significantly lesser in those who consume pieces of Potentilla fulgens root along with RAN. Since evaluation of anticancer role, if any, of P. fulgens on RAN-mediated genetic alterations in human is difficult because of other compounding factors, this study was undertaken in mice to focus on gastric carcinogenesis since ad libitum administration of RAN extract with lime in drinking water induced stomach cancer due to greater exposure of its lining. A total of 160 mice were used at different time points and either methanol extract of P. fulgens roots (PRE) or mixture of four compounds of ethyl-acetate fraction (EA-mixture) was mixed with mice feed. Histological studies revealed that RAN + lime induced cancer in all the mice and interestingly only 20% developed cancer when PRE/EA-mixture was provided along with RAN + lime. Higher frequency of precocious anaphase and over expression of p53 and Securin genes were significantly reduced by PRE/EA-mixture. Thus PRE/EA-mixture mitigates the RAN-induced tumor-initiating process in stomach by maintaining expression of tumor suppressor and check-point genes under control.

Molecular mechanism of celastrol in the treatment of systemic lupus erythematosus based on network pharmacology and molecular docking technology

BACKGROUND

Network pharmacology uses bioinformatics to broaden our understanding of drug actions and thereby advance drug discovery. Here we apply network pharmacology to generate testable hypotheses about the multi-target mechanism of celastrol against systemic lupus erythematosus (SLE).

METHODS

We reconstructed drug-target pathways and networks to predict the likely protein targets of celastrol and the main interactions between those targets and the drug. Then we validated our predictions of candidate targets by performing docking studies with celastrol.

RESULTS

The results suggest that celastrol acts against SLE by regulating the function of several signaling proteins, such as interleukin 10, tumor necrosis factor, and matrix metalloprotein 9, which regulate signaling pathways involving mitogen-activated protein kinase and tumor necrosis factor as well as apoptosis pathways. Celastrol is predicted to affect networks involved mainly in cytokine activity, cytokine receptor binding, receptor ligand activity, receptor regulator activity, and cofactor binding. Molecular docking analysis showed that hydrogen bonding and π-π stacking were the main forms of interaction.

CONCLUSIONS

This network pharmacology strategy may be useful for discovery of multi-target drugs against complex diseases, specifically, it provides protein targets associated with SLE that may be further tested for therapeutic potential by celastrol.

Oncolytic adenovirus programmed by synthetic gene circuit for cancer immunotherapy

Improving efficacy of oncolytic virotherapy remains challenging due to difficulty increasing specificity and immune responses against cancer and limited understanding of its population dynamics. Here, we construct programmable and modular synthetic gene circuits to control adenoviral replication and release of immune effectors selectively in hepatocellular carcinoma cells in response to multiple promoter and microRNA inputs. By performing mouse model experiments and computational simulations, we find that replicable adenovirus has a superior tumor-killing efficacy than non-replicable adenovirus. We observe a synergistic effect on promoting local lymphocyte cytotoxicity and systematic vaccination in immunocompetent mouse models by combining tumor lysis and secretion of immunomodulators. Furthermore, our computational simulations show that oncolytic virus which encodes immunomodulators can exert a more robust therapeutic efficacy than combinatorial treatment with oncolytic virus and immune effector. Our results provide an effective strategy to engineer oncolytic adenovirus, which may lead to innovative immunotherapies for a variety of cancers.

It is difficult to improve the efficacy of oncolytic virotherapy due to immune system responses and limited understanding of population dynamics. Here the authors use synthetic biology gene circuits to control adenoviral replication and release of immunomodulators in hepatocellular carcinoma cells.

A novel approach based on metabolomics coupled with network pharmacology to explain the effect mechanisms of Danggui Buxue Tang in anaemia

Danggui Buxue Tang (DBT) is a famous Chinese medicinal decoction. Mechanism of DBT action is wide ranging and unclear. Exploring new ways of treatment with DBT is useful. Sprague-Dawley(SD) rats were randomly divided into 3 groups including control (NC, Saline), the DBT (at a dose of 8.10 g^-1), and blood deficiency(BD) (Cyclophosphamide (APH)-andCyclophosphamide(CTX)-induced anaemia). A metabolomics approach using Liquid Chromatography-Quadrupole-Time-of-Flight/Mass Spectrometry (LC/Q-TOFMS) was developed to perform the plasma metabolic profiling analysis and differential metaboliteswerescreened according to the multivariate statistical analysiscomparing the NC and BD groups, andthe hub metabolites were outliers with high scores of the centrality indices. Anaemia disease-related protein target and compound of DBT databases were constructed. The TCMSP, ChemMapper and STITCH databases were used to predict the protein targets of DBT. Using the Cytoscape 3.2.1 to establish a phytochemical component-target protein interaction network and establish a component, protein and hub metabolite protein-protein interaction (PPI) network and merging the three PPI networks basing on BisoGenet. The gene enrichment analysis was used to analyse the relationship between proteins based on the relevant genetic similarity by ClueGO. The results shown DBT effectively treated anaemia in vivo. 11 metabolic pathways are involved in the therapeutic effect of DBT in vivo; S-adenosyl-l-methionine, glycine, l-cysteine, arachidonic acid (AA) and phosphatidylcholine(PC) were screened as hub metabolites in APH-and CTX-induced anaemia. A total of 288 targets were identified as major candidates for anaemia progression. The gene-set enrichment analysis revealed that the targets are involved in iron ion binding, haemopoiesis, reactive oxygen species production, inflammation and apoptosis. The results also showed that these targets were associated with iron ion binding, haemopoiesis, ROS production, apoptosis, inflammation and related signalling pathways. DBT can promote iron ion binding and haemopoiesis activities, restrain inflammation, production of reactive oxygen, block apoptosis, and contribute significantly to the DBT treat anaemia.

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.

Crosstalk Between DNA Damage and Inflammation in the Multiple Steps of Gastric Carcinogenesis

Over the last years, intensive investigations in molecular biology and cell physiology extended tremendously the knowledge about the association of inflammation and cancer. In frame of this paradigm, the human pathogen Helicobacter pylori triggers gastritis and gastric ulcer disease, and contributes to the development of gastric cancer. Mechanisms, by which the bacteria-induced inflammation in gastric mucosa leads to intestinal metaplasia and carcinoma, are represented in this review. An altered cell-signaling response and increased production of free radicals by epithelial and immune cells account for the accumulation of DNA damage in gastric mucosa, if infection stays untreated. Host genetics and environmental factors, especially diet, can accelerate the process, which offers the opportunity of intervention based on a balanced nutrition. It is supposed that inflammation might influence stem- or progenitor cells in gastric tissue predisposing for metaplasia or tumor relapse. Herein, DNA is strongly mutated and labile, which restricts therapy options. Thus, the understanding of the mechanisms that underlie gastric carcinogenesis will be of preeminent importance for the development of strategies for screening and early detection. As most gastric cancer patients face late-stage disease with a poor overall survival, the development of multi-targeted therapeutic intervention strategies is a major challenge for the future.

Tongue coating microbiome as a potential biomarker for gastritis including precancerous cascade

The development of gastritis is associated with an increased risk of gastric cancer. Current invasive gastritis diagnostic methods are not suitable for monitoring progress. In this work based on 78 gastritis patients and 50 healthy individuals, we observed that the variation of tongue-coating microbiota was associated with the occurrence and development of gastritis. Twenty-one microbial species were identified for differentiating tongue-coating microbiomes of gastritis and healthy individuals. Pathways such as microbial metabolism in diverse environments, biosynthesis of antibiotics and bacterial chemotaxis were up-regulated in gastritis patients. The abundance of Campylobacter concisus was found associated with the gastric precancerous cascade. Furthermore, Campylobacter concisus could be detected in tongue coating and gastric fluid in a validation cohort containing 38 gastritis patients. These observations provided biological evidence of tongue diagnosis in traditional Chinese medicine, and indicated that tongue-coating microbiome could be a potential non-invasive biomarker, which might be suitable for long-term monitoring of gastritis.

Network pharmacology-based identifcation of potential targets of the flower of Trollius chinensis Bunge acting on anti-inflammatory effectss

The flower of Trollius chinensis Bunge was widely used for the treatment of inflammation-related diseases in traditional Chinese medicine (TCM). In order to clarify the anti-inflammatory mechanism of this Chinese herbs, a comprehensive network pharmacology strategy that consists of three sequential modules (pharmacophore matching, enrichment analysis and molecular docking.) was carried out. As a result, Apoptosis signal-regulating kinase 1 (ASK1), Janus kinase 1 (JAK1), c-Jun N-terminal kinases (JNKs), transforming protein p21 (HRas) and mitogen-activated protein kinase 14 (p38α) that related to the anti-inflammatory effect were filtered out. In further molecular dynamics (MD) simulation, the conformation of CID21578038 and CID20055288 were found stable in the protein ASK1 and JNKs respectively. The current investigation revealed that two effective compounds in the flower of Trollius chinensis Bunge played a crucial role in the process of inflammation by targeting ASK1 and JNKs, the comprehensive strategy can serve as a universal method to guide in illuminating the mechanism of the prescription of traditional Chinese medicine by identifying the pathways or targets.

Network analysis and mechanisms of action of Chinese herb-related natural compounds in lung cancer cells

BACKGROUND

Chinese herbal medicines (CHMs) are a resource of natural compounds (ingredients) and their potential chemical derivatives with anticancer properties, some of which are already in clinical use. Bei-Mu (BM), Jie-Geng (JG), and Mai-Men-Dong-Tang (MMDT) are important CHMs prescribed for patients with lung cancer that have improved the survival rate.

HYPOTHESIS/PURPOSE

The aim of this study was to systemically investigate the mechanisms of action of these CHM products in lung cancer cells.

METHODS

We used a network pharmacology approach to study CHM product-related natural compounds and their lung cancer targets. In addition, the underlying anti-lung cancer effects of the natural compounds on apoptosis, cell cycle progression, autophagy, and the expression of related proteins was investigated in vitro.

RESULTS

Ingredient-lung cancer target network analysis identified 20 natural compounds. Three of these compounds, ursolic acid, 2-(3R)-8,8-dimethyl-3,4-dihydro-2H-pyrano(6,5-f)chromen-3-yl)-5-methoxyphenol, and licochalcone A, inhibited the proliferation of A549 lung cancer cells in a dose-dependent manner. Signal pathway analyses suggested that these three ingredients may target cellular apoptosis, anti-apoptosis, and cell cycle-related proteins. These three ingredients induced apoptosis through the regulation of the expression of apoptotic and anti-apoptotic proteins, including B-cell lymphoma-2 and full-length and cleaved poly(ADP-ribose) polymerase proteins. They also induced cell cycle arrest in S and G2/M phases and autophagy in A549 cells.

CONCLUSION

The pharmacological mechanisms of ingredients from MMDT on lung cancer may be strongly associated with their modulatory effects on apoptosis, autophagy, cell cycle progression, and cell proliferation.

A Combined Phytochemistry and Network Pharmacology Approach to Reveal the Potential Antitumor Effective Substances and Mechanism of Phellinus igniarius

Phellinus igniarius (P. igniarius) is a medicinal fungus that is widely used in East Asia for the adjuvant treatment of cancer. To elucidate the antitumor effective substances and mechanism of P. igniarius, we designed an approach incorporating cytotoxicity screening, phytochemical analysis, network pharmacology construction, and cellular and molecular experiments. The dichloromethane extract of P. igniarius (DCMPI) was identified as the active portion in HT-29 cells. Nineteen constituents were identified, and 5 were quantified by UPLC-ESI-Q/TOF-MS. Eight ingredients were obtained in the network pharmacology study. In total, 473 putative targets associated with DCMPI and 350 putative targets related to colon cancer were derived from online databases and target prediction tools. Protein-protein interaction networks of drug and disease putative targets were constructed, and 84 candidate targets were identified based on topological features. Pathway enrichment analysis showed that the candidate targets were mostly related to reactive oxygen species (ROS) metabolic processes and intrinsic apoptotic pathways. Then, a cellular experiment was used to validate the drug-target mechanisms predicted by the system pharmacology analysis. Experimental results showed that DCMPI increased intracellular ROS levels and induced HT-29 cell apoptosis. Molecular biology experiments indicated that DCMPI not only increased Bax and Bad protein expression and promoted PARP and caspase-3/9 cleavage but also down-regulated Bcl-2 and Bcl-xl protein levels to induce apoptosis in HT-29 cells. In conclusion, our study provides knowledge on the chemical composition and antitumor mechanism of P. igniarius, which may be exploited as a promising therapeutic option for colon cancer.

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

Tumorigenesis is a complex process that is driven by a combination of networks of genes and environmental factors; however, efficient approaches to identifying functional networks that are perturbed by the process of tumorigenesis are lacking. In this study, we provide a comprehensive network-based strategy for the systematic discovery of functional synergistic modules that are causal determinants of inflammation-induced tumorigenesis. Our approach prioritizes candidate genes selected by integrating clinical-based and network-based genome-wide gene prediction methods and identifies functional synergistic modules based on combinatorial CRISPR-Cas9 screening. On the basis of candidate genes inferred de novo from experimental and computational methods to be involved in inflammation and cancer, we used an existing TGFβ1-induced cellular transformation model in colonic epithelial cells and a new combinatorial CRISPR-Cas9 screening strategy to construct an inflammation-induced differential genetic interaction network. The inflammation-induced differential genetic interaction network that we generated yielded functional insights into the genes and functional module combinations, and showed varied responses to the inflammation agents as well as active traditional Chinese medicine compounds. We identified opposing differential genetic interactions of inflammation-induced tumorigenesis: synergistic promotion and suppression. The synergistic promotion state was primarily caused by deletions in the immune and metabolism modules; the synergistic suppression state was primarily induced by deletions in the proliferation and immune modules or in the proliferation and metabolism modules. These results provide insight into possible early combinational targets and biomarkers for inflammation-induced tumorigenesis and highlight the synergistic effects that occur among immune, proliferation, and metabolism modules. In conclusion, this approach deepens the understanding of the underlying mechanisms that cause inflammation to potentially increase the cancer risk of colonic epithelial cells and accelerate the translation into novel functional modules or synergistic module combinations that modulate complex disease phenotypes.

Stochastic Telomere Shortening and the Route to Limitless Replicative Potential

In human tissues, the replicative potential of stem cells is limited by the shortening of telomere, limitless replicative potential is a hallmark of cancer. Telomere length changes stochastically during cell division mainly due to the competition between the end replication problem and telomerase, short telomere can lead to replicative senescence and cell apoptosis. Here, we investigate how stochastic changes of telomere length in individual cells may affect the population dynamics of clonal growth. We established a computational model that couples telomerase-regulated stochastic telomere length changes with the replicative potential of clones. Model simulations reveal qualitative dependence of clone proliferation potential with activities of telomerase; mutations in cells to alter the activities of telomerase and its inhibitors can induce abnormal tissue growth and lead to limitless replicative potential.

A New Model System for Exploring Assembly Mechanisms of the HIV-1 Immature Capsid In Vivo

The assembly of the HIV-1 immature capsid (HIC) is an essential step in the virus life cycle. In vivo, the HIC is composed of [Formula: see text] hexameric building blocks, and it takes 5-6 min to complete the assembly process. The involvement of numerous building blocks and the rapid timecourse makes it difficult to understand the HIC assembly process. In this work, we study HIC assembly in vivo by using differential equations. We first obtain a full model with 420 differential equations. Then, we reduce six addition reactions for separate building blocks to a single complex reaction. This strategy reduces the full model to 70 equations. Subsequently, the theoretical analysis of the reduced model shows that it might not be an effective way to decrease the HIC concentration at the equilibrium state by decreasing the microscopic on-rate constants. Based on experimental data, we estimate that the nucleating structure is much smaller than the HIC. We also estimate that the microscopic on-rate constant for nucleation reactions is far less than that for elongation reactions. The parametric collinearity investigation testifies the reliability of these two characteristics, which might explain why free building blocks do not readily polymerize into higher-order polymers until their concentration reaches a threshold value. These results can provide further insight into the assembly mechanisms of the HIC in vivo.

Computational Modeling of the Crosstalk Between Macrophage Polarization and Tumor Cell Plasticity in the Tumor Microenvironment

Tumor microenvironments contain multiple cell types interacting among one another via different signaling pathways. Furthermore, both cancer cells and different immune cells can display phenotypic plasticity in response to these communicating signals, thereby leading to complex spatiotemporal patterns that can impact therapeutic response. Here, we investigate the crosstalk between cancer cells and macrophages in a tumor microenvironment through in silico (computational) co-culture models. In particular, we investigate how macrophages of different polarization (M₁ vs. M₂) can interact with epithelial-mesenchymal plasticity of cancer cells, and conversely, how cancer cells exhibiting different phenotypes (epithelial vs. mesenchymal) can influence the polarization of macrophages. Based on interactions documented in the literature, an interaction network of cancer cells and macrophages is constructed. The steady states of the network are then analyzed. Various interactions were removed or added into the constructed-network to test the functions of those interactions. Also, parameters in the mathematical models were varied to explore their effects on the steady states of the network. In general, the interactions between cancer cells and macrophages can give rise to multiple stable steady-states for a given set of parameters and each steady state is stable against perturbations. Importantly, we show that the system can often reach one type of stable steady states where cancer cells go extinct. Our results may help inform efficient therapeutic strategies.

Integrating Network Pharmacology and Metabolomics Study on Anti-rheumatic Mechanisms and Antagonistic Effects Against Methotrexate-Induced Toxicity of Qing-Luo-Yin

Qing-Luo-Yin (QLY) is a traditional Chinese medicine (TCM) formula used to treat Hot Syndrome-related rheumatoid arthritis (RA). Previously, we uncovered partial mechanisms involved in the therapeutic actions of QLY on RA. In this study, we further elucidated its anti-rheumatic mechanisms and investigated its possible interactions with methotrexate (MTX) in vivo using an integrating strategy coupled with network pharmacology and metabolomics techniques. Chemical composition of QLY was characterized by HPLC analysis. Collagen induced arthritis (CIA) was developed in male SD rats. The CIA rats were then assigned into different groups, and received QLY, MTX or QLY+MTX treatments according to the pre-arrangement. Therapeutic effects of QLY and its possible interactions with MTX in vivo were evaluated by clinical parameters, digital radiography assessment, histological/immunohistochemical examination, and serological biomarkers. Mechanisms underlying these actions were deciphered with network pharmacology methods, and further validated by metabolomics clues based on UPLC-Q-TOF/MS analysis of urines. Experimental evidences demonstrated that QLY notably alleviated the severity of CIA and protected joints from destruction. Re-balanced levels of hemoglobin and alanine transaminase in serum indicated reduced MTX-induced hepatic injury and myelosuppression under the co-treatment of QLY. Network-based target prediction found dozens of RA related proteins as potential targets of QLY. Upon the further biological function enrichment analysis, we found that a large amount of them were involved in nucleotide metabolism and immune functions. Metabolomics analysis showed that QLY restored amino acids, fatty acids, and energy metabolisms in CIA rats, which solidly supported its therapeutic effects on CIA. Consistently to findings from network pharmacology analysis, metabolomics study also found altered purine, pyrimidine, and pentose phosphate metabolisms in CIA rats receiving QLY treatment. All these clues suggested that inhibition on nucleic acid synthesis was essential to the immunosuppressive activity of QLY in vivo, and could contribute great importance to its therapeutic effects on CIA. Additionally, QLY induced significant antifolate resistance in rats, which would prevent folate from depletion during long-term MTX treatment, and should account for reduced side effects in combination regimen with MTX and QLY.

Neural network analysis of Chinese herbal medicine prescriptions for patients with colorectal cancer

Traditional Chinese Medicine (TCM) is an experiential form of medicine with a history dating back thousands of years. The present study aimed to utilize neural network analysis to examine specific prescriptions for colorectal cancer (CRC) in clinical practice to arrive at the most effective prescription strategy. The study analyzed the data of 261 CRC cases recruited from a total of 141,962 cases of renowned veteran TCM doctors collected from datasets of both the DeepMedic software and TCM cancer treatment books. The DeepMedic software was applied to normalize the symptoms/signs and Chinese herbal medicine (CHM) prescriptions using standardized terminologies. Over 20 percent of CRC patients demonstrated symptoms of poor appetite, fatigue, loose stool, and abdominal pain. By analyzing the prescription patterns of CHM, we found that Atractylodes macrocephala (Bai-zhu) and Poria (Fu-ling) were the most commonly prescribed single herbs identified through analysis of medical records, and supported by the neural network analysis; although there was a slight difference in the sequential order. The study revealed an 81.9% degree of similarity of CHM prescriptions between the medical records and the neural network suggestions. The patterns of nourishing Qi and eliminating dampness were the most common goals of clinical prescriptions, which corresponds with treatments of CRC patients in clinical practice. This is the first study to employ machine learning, specifically neural network analytics to support TCM clinical diagnoses and prescriptions. The DeepMedic software may be used to deliver accurate TCM diagnoses and suggest prescriptions to treat CRC.

Network Pharmacology to Unveil the Biological Basis of Health-Strengthening Herbal Medicine in Cancer Treatment

Health-strengthening (Fu-Zheng) herbs is a representative type of traditional Chinese medicine (TCM) widely used for cancer treatment in China, which is in contrast to pathogen eliminating (Qu-Xie) herbs. However, the commonness in the biological basis of health-strengthening herbs remains to be holistically elucidated. In this study, an innovative high-throughput research strategy integrating computational and experimental methods of network pharmacology was proposed, and 22 health-strengthening herbs were selected for the investigation. Additionally, 25 pathogen-eliminating herbs were included for comparison. First, based on network-based, large-scale target prediction, we analyzed the target profiles of 1446 TCM compounds. Next, the actions of 166 compounds on 420 antitumor or immune-related genes were measured using a unique high-throughput screening strategy by high-throughput sequencing, referred to as HTS². Furthermore, the structural information and the antitumor activity of the compounds in health-strengthening and pathogen-eliminating herbs were compared. Using network pharmacology analysis, we discovered that: (1) Functionally, the predicted targets of compounds from health strengthening herbs were enriched in both immune-related and antitumor pathways, similar to those of pathogen eliminating herbs. As a case study, galloylpaeoniflorin, a compound in a health strengthening herb Radix Paeoniae Alba (Bai Shao), was found to exert antitumor effects both in vivo and in vitro. Yet the inhibitory effects of the compounds from pathogen eliminating herbs on tumor cells proliferation as a whole were significantly stronger than those in health-strengthening herbs (p < 0.001). Moreover, the percentage of assay compounds in health-strengthening herbs with the predicted targets enriched in the immune-related pathways (e.g., natural killer cell mediated cytotoxicity and antigen processing and presentation) were significantly higher than that in pathogen-eliminating herbs (p < 0.05). This finding was supported by the immune-enhancing effects of a group of compounds from health-strengthening herbs indicated by differentially expressed genes in the HTS² results. (2) Compounds in the same herb may exhibit the same or distinguished mechanisms in cancer treatment, which was demonstrated as the compounds influence pathway gene expressions in the same or opposite directions. For example, acetyl ursolic acid and specnuezhenide in a health-strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi) both upregulated gene expressions in T cell receptor signaling pathway. Together, this study suggested greater potentials in tumor immune microenvironment regulation and tumor prevention than in direct killing tumor cells of health-strengthening herbs generally, and provided a systematic strategy for unveiling the commonness in the biological basis of health-strengthening herbs in cancer treatment.

A Systems-Level Analysis of Mechanisms of Platycodon grandiflorum Based on A Network Pharmacological Approach

Platycodon grandiflorum (PG) is widely used in Asia for its various beneficial effects. Although many studies were conducted to understand the molecular mechanisms of PG, it is still unclear how the combinations of multiple ingredients work together to exert its therapeutic effects. The aim of the present study was to provide a comprehensive review of the systems-level mechanisms of PG by adopting network pharmacological analysis. We constructed a compound⁻target⁻disease network for PG using experimentally validated and machine-leaning-based prediction results. Each target of the network was analyzed based on previously known pharmacological activities of PG. Gene ontology analysis revealed that the majority of targets were related to cellular and metabolic processes, responses to stimuli, and biological regulation. In pathway enrichment analyses of targets, the terms related to cancer showed the most significant enrichment and formed distinct clusters. Degree matrix analysis for target⁻disease associations of PG suggested the therapeutic potential of PG in various cancers including hepatocellular carcinoma, gastric cancer, prostate cancer, small-cell lung cancer, and renal cell carcinoma. We expect that network pharmacological approaches will provide an understanding of the systems-level mechanisms of medicinal herbs and further develop their therapeutic potentials.

A network pharmacology-based approach to analyse potential targets of traditional herbal formulas: An example of Yu Ping Feng decoction

Herbal formulas from traditional Chinese medicines (TCMs) have been extensively used in clinics as effective therapies, but it is still a great challenge to demonstrate the scientific basis for their therapeutic effects at the level of molecular biology. By taking a classic herbal formula (Yu Ping Feng decoction, YPF) as an example, this study developed a novel network pharmacology based method to identify its potential therapeutic targets. First, this study constructed a “targets–(pathways)–targets” (TPT) network in which targets of YPF were connected by relevant pathways; then, this network was decomposed into separate modules with strong internal connections; lastly, the propensity of each module toward different diseases was assessed by a contribution score. On the basis of a significant association between network modules and therapeutic diseases validated by chi-square test (p-value < 0.001), this study identified the network module with the strongest propensity toward therapeutic diseases of YPF. Further, the targets with the highest centrality in this module are recommended as YPF’s potential therapeutic targets. By integrating the complicated “multi-targets–multi-pathways–multi-diseases” relationship of herbal formulas, the method shows promise for identifying its potential therapeutic targets, which could contribute to the modern scientific illustration of TCMs’ traditional clinical applications.