cGAS-STING, inflammasomes and pyroptosis: an overview of crosstalk mechanism of activation and regulation.

BACKGROUND: Intracellular DNA-sensing pathway cGAS-STING, inflammasomes and pyroptosis act as critical natural immune signaling axes for microbial infection, chronic inflammation, cancer progression and organ degeneration, but the mechanism and regulation of the crosstalk network remain unclear. Cellular stress disrupts mitochondrial homeostasis, facilitates the opening of mitochondrial permeability transition pore and the leakage of mitochondrial DNA to cell membrane, triggers inflammatory responses by activating cGAS-STING signaling, and subsequently induces inflammasomes activation and the onset of pyroptosis. Meanwhile, the inflammasome-associated protein caspase-1, Gasdermin D, the CARD domain of ASC and the potassium channel are involved in regulating cGAS-STING pathway. Importantly, this crosstalk network has a cascade amplification effect that exacerbates the immuno-inflammatory response, worsening the pathological process of inflammatory and autoimmune diseases. Given the importance of this crosstalk network of cGAS-STING, inflammasomes and pyroptosis in the regulation of innate immunity, it is emerging as a new avenue to explore the mechanisms of multiple disease pathogenesis. Therefore, efforts to define strategies to selectively modulate cGAS-STING, inflammasomes and pyroptosis in different disease settings have been or are ongoing. In this review, we will describe how this mechanistic understanding is driving possible therapeutics targeting this crosstalk network, focusing on the interacting or regulatory proteins, pathways, and a regulatory mitochondrial hub between cGAS-STING, inflammasomes, and pyroptosis. SHORT CONCLUSION: This review aims to provide insight into the critical roles and regulatory mechanisms of the crosstalk network of cGAS-STING, inflammasomes and pyroptosis, and to highlight some promising directions for future research and intervention.

iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

Characterizing Intercellular Communication of Pan-Cancer Reveals SPP1+ Tumor-Associated Macrophage Expanded in Hypoxia and Promoting Cancer Malignancy Through Single-Cell RNA-Seq Data.

Hypoxia is a characteristic of tumor microenvironment (TME) and is a major contributor to tumor progression. Yet, subtype identification of tumor-associated non-malignant cells at single-cell resolution and how they influence cancer progression under hypoxia TME remain largely unexplored. Here, we used RNA-seq data of 424,194 single cells from 108 patients to identify the subtypes of cancer cells, stromal cells, and immune cells; to evaluate their hypoxia score; and also to uncover potential interaction signals between these cells in vivo across six cancer types. We identified SPP1+ tumor-associated macrophage (TAM) subpopulation potentially enhanced epithelial-mesenchymal transition (EMT) by interaction with cancer cells through paracrine pattern. We prioritized SPP1 as a TAM-secreted factor to act on cancer cells and found a significant enhanced migration phenotype and invasion ability in A549 lung cancer cells induced by recombinant protein SPP1. Besides, prognostic analysis indicated that a higher expression of SPP1 was found to be related to worse clinical outcome in six cancer types. SPP1 expression was higher in hypoxia-high macrophages based on single-cell data, which was further validated by an in vitro experiment that SPP1 was upregulated in macrophages under hypoxia-cultured compared with normoxic conditions. Additionally, a differential analysis demonstrated that hypoxia potentially influences extracellular matrix remodeling, glycolysis, and interleukin-10 signal activation in various cancer types. Our work illuminates the clearer underlying mechanism in the intricate interaction between different cell subtypes within hypoxia TME and proposes the guidelines for the development of therapeutic targets specifically for patients with high proportion of SPP1+ TAMs in hypoxic lesions.

CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer.

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).

Coptisine from Coptis chinensis exerts diverse beneficial properties: A concise review.

Coptisine is a natural small-molecular compound extracted from Coptis chinensis (CC) with a history of using for thousands of years. This work aimed at summarizing coptisine's activity and providing advice for its clinical use. We analysed the online papers in the database of SciFinder, Web of Science, PubMed, Google scholar and CNKI by setting keywords as 'coptisine' in combination of 'each pivotal pathway target'. Based on the existing literatures, we find (a) coptisine exerted potential to be an anti-cancer, anti-inflammatory, CAD ameliorating or anti-bacterial drug through regulating the signalling transduction of pathways such as NF-κB, MAPK, PI3K/Akt, NLRP3 inflammasome, RANKL/RANK and Beclin 1/Sirt1. However, we also (b) observe that the plasma concentration of coptisine demonstrates obvious non-liner relationship with dosage, and even the highest dosage used in animal study actually cannot reach the minimum concentration level used in cell experiments owing to the poor absorption and low availability of coptisine. We conclude (a) further investigations can focus on coptisine's effect on caspase-1-involved inflammasome assembling and pyroptosis activation, as well as autophagy. (b) Under circumstance of promoting coptisine availability by pursuing nano- or microrods strategies or applying salt-forming process to coptisine, can it be introduced to clinical trial.

Crosstalk network among multiple inflammatory mediators in liver fibrosis.

Liver fibrosis is the common pathological basis of all chronic liver diseases, and is the necessary stage for the progression of chronic liver disease to cirrhosis. As one of pathogenic factors, inflammation plays a predominant role in liver fibrosis via communication and interaction between inflammatory cells, cytokines, and the related signaling pathways. Damaged hepatocytes induce an increase in pro-inflammatory factors, thereby inducing the development of inflammation. In addition, it has been reported that inflammatory response related signaling pathway is the main signal transduction pathway for the development of liver fibrosis. The crosstalk regulatory network leads to hepatic stellate cell activation and proinflammatory cytokine production, which in turn initiate the fibrotic response. Compared with the past, the research on the pathogenesis of liver fibrosis has been greatly developed. However, the liver fibrosis mechanism is complex and many pathways involved need to be further studied. This review mainly focuses on the crosstalk regulatory network among inflammatory cells, cytokines, and the related signaling pathways in the pathogenesis of chronic inflammatory liver diseases. Moreover, we also summarize the recent studies on the mechanisms underlying liver fibrosis and clinical efforts on the targeted therapies against the fibrotic response.

Quantifying Risk Pathway Crosstalk Mediated by miRNA to Screen Precision drugs for Breast Cancer Patients.

Breast cancer has become the most common cancer that leads to women's death. Breast cancer is a complex, highly heterogeneous disease classified into various subtypes based on histological features, which determines the therapeutic options. System identification of effective drugs for each subtype remains challenging. In this work, we present a computational network biology approach to screen precision drugs for different breast cancer subtypes by considering the impact intensity of candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly, we constructed and analyzed the subtype-specific risk pathway crosstalk networks mediated by miRNAs. Then, we evaluated 36 Food and Drug Administration (FDA)-approved anticancer drugs by quantifying their effects on these subtype-specific pathway crosstalk networks and combining with survival analysis. Finally, some first-line treatments of breast cancer, such as Paclitaxel and Vincristine, were optimized for each subtype. In particular, we performed precision screening of subtype-specific therapeutic drugs and also confirmed some novel drugs suitable for breast cancer treatment. For example, Sorafenib was applicable for the basal subtype treatment, Irinotecan was optimum for Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype treatment, and Vorinostat could apply to LumB subtype treatment. In addition, the mechanism of these optimal therapeutic drugs in each subtype of breast cancer was further dissected. In summary, our study offers an effective way to screen precision drugs for various breast cancer subtype treatments. We also dissected the mechanism of optimal therapeutic drugs, which may provide novel insight into the precise treatment of cancer and promote researches on the mechanisms of action of drugs.

Crosstalk analysis of dysregulated pathways in preeclampsia.

A crosstalk between multiple biological pathways has been proposed in biological processes. However, the existence and degree of this phenomenon in patients with preeclampsia (PE) have not been strictly investigated. Thus, this study explored an dysregulated pathway set (DPS) for PE based on pathway crosstalk network (PCN) related analysis. In the present study, four steps were performed in the inference of DPS: acquiring data of gene expression, pathway and protein-protein interaction (PPI) construction; building a PCN through integrating the information in these datasets and Pearson's correlation coefficient (PCC). A principal component analysis (PCA) approach was used to compute the activity of every pathway for selecting seed pathway of PCN. DPS was evaluated by measuring of an area under the receiver operating characteristics curve (AUC) and seed pathway from PCN. Consequently, a total of 420 pathways and 6,032 crosstalks were mapped to the PCN, in which RIG-I/MDA5-mediated induction of IFN-α/ß pathways was identified as the seed pathway that had the greatest changes in activity scores across PE patients and normal controls. DPS was composed of 15 dysregulated pathways and 46 crosstalks, in which CLEC7A (Dectin-1) signaling possessed the highest degree of 12, which indicated it exerted an important role in the DPS. Our results revealed crosstalk between pathways and the DPS crucial for PE pathogenesis, which aid in excavating potential biomarkers of PE therapy and unveil the underlying pathological mechanism of this disease.

Integrative analysis of lncRNAs and miRNAs with coding RNAs associated with ceRNA crosstalk network in triple negative breast cancer.

Triple negative breast cancer (TNBC) is a particular subtype of breast malignant tumor with poorer prognosis than other molecular subtypes. Currently, there is increasing focus on long non-coding RNAs (lncRNAs), which can act as competing endogenous RNAs (ceR-NAs) and suppress miRNA functions involved in post-transcriptional regulatory networks in the tumor. Therefore, to investigate specific mechanisms of TNBC carcinogenesis and improve treatment efficiency, we comprehensively integrated expression profiles, including data on mRNAs, lncRNAs and miRNAs obtained from 116 TNBC tissues and 11 normal tissues from The Cancer Genome Atlas. As a result, we selected the threshold with |log2FC|>2.0 and an adjusted p-value >0.05 to obtain the differentially expressed mRNAs, miRNAs and lncRNAs. Hereafter, weighted gene co-expression network analysis was performed to identify the expression characteristics of dysregulated genes. We obtained five co-expression modules and related clinical feature. By means of correlating gene modules with protein-protein interaction network analysis that had identified 22 hub mRNAs which could as hub target genes. Eleven key dysregulated differentially expressed micro RNAs (DEmiRNAs) were identified that were significantly associated with the 22 hub potential target genes. Moreover, we found that 14 key differentially expressed lncRNAs could interact with the key DEmiRNAs. Then, the ceRNA crosstalk network of TNBC was constructed by utilizing key lncRNAs, key miRNAs, and hub mRNAs in Cytoscape software. We analyzed and described the potential characteristics of biological function and pathological roles of the TNBC ceRNA co-regulatory network; also, the survival analysis was performed for each molecule. These findings revealed that ceRNA crosstalk network could play an important role in the development and progression for TNBC. In addition, we also identified that some molecules in the ceRNA network possess clinical correlation and prognosis.

A Comprehensive and System Review for the Pharmacological Mechanism of Action of Rhein, an Active Anthraquinone Ingredient.

Rhein is a major medicinal ingredient isolated from several traditional Chinese medicines, including Rheum palmatum L., Aloe barbadensis Miller, Cassia angustifolia Vahl., and Polygonum multiflorum Thunb. Rhein has various pharmacological activities, such as anti-inflammatory, antitumor, antioxidant, antifibrosis, hepatoprotective, and nephroprotective activities. Although more than 100 articles in PubMed are involved in the pharmacological mechanism of action of rhein, only a few focus on the relationship of crosstalk among multiple pharmacological mechanisms. The mechanism of rhein involves multiple pathways which contain close interactions. From the overall perspective, the pathways which are related to the targets of rhein, are initiated by the membrane receptor. Then, MAPK and PI3K-AKT parallel signaling pathways are activated, and several downstream pathways are affected, thereby eventually regulating cell cycle and apoptosis. The therapeutic effect of rhein, as a multitarget molecule, is the synergistic and comprehensive result of the involvement of multiple pathways rather than the blocking or activation of a single signaling pathway. We review the pharmacological mechanisms of action of rhein by consulting literature published in the last 100 years in PubMed. We then summarize these pharmacological mechanisms from a comprehensive, interactive, and crosstalk perspective. In general, the molecular mechanism of action of drug must be understood from a systematic and holistic perspective, which can provide a theoretical basis for precise treatment and rational drug use.

Evaluating Alzheimer's Disease Progression by Modeling Crosstalk Network Disruption.

Aß, tau, and P-tau have been widely accepted as reliable markers for Alzheimer's disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated by network disruption probability. Presented results show that network disruption probability has a good linear relationship with Mini Mental State Examination (MMSE). The proposed model combined with Support vector machine (SVM) achieves a relative high 10-fold cross-validated performance in classification of AD vs. normal and mild cognitive impairment (MCI) vs. normal (95% accuracy, 95% sensitivity, 95% specificity for AD vs. normal; 90% accuracy, 94% sensitivity, 83% specificity for MCI vs. normal). This research evaluates the progression of AD and facilitates AD early diagnosis.