Honeysuckle (Lonicera japonica) and Huangqi (Astragalus membranaceus) Suppress SARS-CoV-2 Entry and COVID-19 Related Cytokine Storm in Vitro.

COVID-19 is threatening human health worldwide but no effective treatment currently exists for this disease. Current therapeutic strategies focus on the inhibition of viral replication or using anti-inflammatory/immunomodulatory compounds to improve host immunity, but not both. Traditional Chinese medicine (TCM) compounds could be promising candidates due to their safety and minimal toxicity. In this study, we have developed a novel in silico bioinformatics workflow that integrates multiple databases to predict the use of honeysuckle (Lonicera japonica) and Huangqi (Astragalus membranaceus) as potential anti-SARS-CoV-2 agents. Using extracts from honeysuckle and Huangqi, these two herbs upregulated a group of microRNAs including let-7a, miR-148b, and miR-146a, which are critical to reduce the pathogenesis of SARS-CoV-2. Moreover, these herbs suppressed pro-inflammatory cytokines including IL-6 or TNF-α, which were both identified in the cytokine storm of acute respiratory distress syndrome, a major cause of COVID-19 death. Furthermore, both herbs partially inhibited the fusion of SARS-CoV-2 spike protein-transfected BHK-21 cells with the human lung cancer cell line Calu-3 that was expressing ACE2 receptors. These herbs inhibited SARS-CoV-2 Mpro activity, thereby alleviating viral entry as well as replication. In conclusion, our findings demonstrate that honeysuckle and Huangqi have the potential to be used as an inhibitor of SARS-CoV-2 virus entry that warrants further in vivo analysis and functional assessment of miRNAs to confirm their clinical importance. This fast-screening platform can also be applied to other drug discovery studies for other infectious diseases.

Seeding-inspired chemotaxis genetic algorithm for the inference of biological systems.

A large challenge in the post-genomic era is to obtain the quantitatively dynamic interactive information of the important constitutes of underlying systems. The S-system is a dynamic and structurally rich model that determines the net strength of interactions between genes and/or proteins. Good generation characteristics without the need for prior information have allowed S-systems to become one of the most promising canonical models. Various evolutionary computation technologies have recently been developed for the identification of system parameters and skeletal-network structures. However, the gaps between the truncated and preserved terms remain too small. Additionally, current research methods fail to identify the structures of high dimensional systems (e.g., 30 genes with 1800 connections). Optimization technologies should converge fast and have the ability to adaptively adjust the search. In this study, we propose a seeding-inspired chemotaxis genetic algorithm (SCGA) that can force evolution to adjust the population movement to identify a favorable location. The seeding-inspired training strategy is a method to achieve optimal results with limited resources. SCGA introduces seeding-inspired genetic operations to allow a population to possess competitive power (exploitation and exploration) and a winner-chemotaxis-induced population migration to force a population to repeatedly tumble away from an attractor and swim toward another attractor. SCGA was tested on several canonical biological systems. SCGA not only learned the correct structure within only one to three pruning steps but also ensures pruning safety. The values of the truncated terms were all smaller than 10-14, even for a thirty-gene system.

Autophagy-preferential degradation of MIR224 participates in hepatocellular carcinoma tumorigenesis.

Autophagy and microRNA (miRNA) are important regulators during cancer cell tumorigenesis. Impaired autophagy and high expression of the oncogenic microRNA MIR224 are prevalent in hepatocellular carcinoma (HCC); however, the relationship between the 2 phenomena remains elusive. In this study, we are the first to reveal that autophagy selectively regulates MIR224 expression through an autophagosome-mediated degradation system. Based on this finding, we further demonstrated that in hepatitis B virus (HBV)-related HCC, aberrant autophagy (low autophagic activity) results in accumulation of MIR224 and decreased expression of the target gene Smad4, which leads to increased cell migration and tumor formation. Preferential recruitment of MIR224 into the autophagosome was clearly demonstrated by a) miRNA in situ hybridization under confocal microscopy, and b) immunogold labeling of MIR224 under electron microscopy compared with a ubiquitously expressed microRNA MIRlet7e/let-7. Furthermore, we found that off-label use of amiodarone, an antiarrhythmic agent, effectively suppressed HCC tumorigenesis through autophagy-mediated MIR224 degradation both in vitro and in vivo. In summary, we identified amiodarone as a new autophagy inducer, which may provide an alternative approach in HCC therapy through a novel tumor suppression mechanism.

Autophagy suppresses tumorigenesis of hepatitis B virus-associated hepatocellular carcinoma through degradation of microRNA-224.

UNLABELLED: In hepatocellular carcinoma (HCC), dysregulated expression of microRNA-224 (miR-224) and impaired autophagy have been reported separately. However, the relationship between them has not been explored. In this study we determined that autophagy is down-regulated and inversely correlated with miR-224 expression in hepatitis B virus (HBV)-associated HCC patient specimens. These results were confirmed in liver tumors of HBV X gene transgenic mice. Furthermore, miR-224 was preferentially recruited and degraded during autophagic progression demonstrated by real-time polymerase chain reaction and miRNA in situ hybridization electron microscopy after extraction of autophagosomes. Our in vitro study demonstrated that miR-224 played an oncogenic role in hepatoma cell migration and tumor formation through silencing its target gene Smad4. In HCC patients, the expression of low-Atg5, high-miR-224, and low-Smad4 showed significant correlation with HBV infection and a poor overall survival rate. Autophagy-mediated miR-224 degradation and liver tumor suppression were further confirmed by the autophagy inducer amiodarone and miR-224 antagonist using an orthotopic SD rat model. CONCLUSION: A noncanonical pathway links autophagy, miR-224, Smad4, and HBV-associated HCC. These findings open a new avenue for the treatment of HCC.

Computational optimization for S-type biological systems: cockroach genetic algorithm.

S-type biological systems (S-systems) are demonstrated to be universal approximations of continuous biological systems. S-systems are easy to be generalized to large systems. The systems are identified through data-driven identification techniques (cluster-based algorithms or computational methods). However, S-systems' identification is challenging because multiple attractors exist in such highly nonlinear systems. Moreover, in some biological systems the interactive effect cannot be neglected even the interaction order is small. Therefore, learning should be focused on increasing the gap between the true and redundant interaction. In addition, a wide searching space is necessary because no prior information is provided. The used technologies should have the ability to achieve convergence enhancement and diversity preservation. Cockroaches live in nearly all habitats and survive for more than 300 million years. In this paper, we mimic cockroaches' competitive swarm behavior and integrated it with advanced evolutionary operations. The proposed cockroach genetic algorithm (CGA) possesses strong snatching-food ability to rush forward to a target and high migration ability to escape from local minimum. CGA was tested with three small-scale systems, a twenty-state medium-scale system and a thirty-state large-scale system. A wide search space ([0,100] for rate constants and [-100,100] for kinetic orders) with random or bad initial starts are used to show the high exploration performance.

Fine-tuning of microRNA-mediated repression of mRNA by splicing-regulated and highly repressive microRNA recognition element.

BACKGROUND: MicroRNAs are very small non-coding RNAs that interact with microRNA recognition elements (MREs) on their target messenger RNAs. Varying the concentration of a given microRNA may influence the expression of many target proteins. Yet, the expression of a specific target protein can be fine-tuned by alternative cleavage and polyadenylation to the corresponding mRNA. RESULTS: This study showed that alternative splicing of mRNA is a fine-tuning mechanism in the cellular regulatory network. The splicing-regulated MREs are often highly repressive MREs. This phenomenon was observed not only in the hsa-miR-148a-regulated DNMT3B gene, but also in many target genes regulated by hsa-miR-124, hsa-miR-1, and hsa-miR-181a. When a gene contains multiple MREs in transcripts, such as the VEGF gene, the splicing-regulated MREs are again the highly repressive MREs. Approximately one-third of the analysable human MREs in MiRTarBase and TarBase can potentially perform the splicing-regulated fine-tuning. Interestingly, the high (+30%) repression ratios observed in most of these splicing-regulated MREs indicate associations with functions. For example, the MRE-free transcripts of many oncogenes, such as N-RAS and others may escape microRNA-mediated suppression in cancer tissues. CONCLUSIONS: This fine-tuning mechanism revealed associations with highly repressive MRE. Since high-repression MREs are involved in many important biological phenomena, the described association implies that splicing-regulated MREs are functional. A possible application of this observed association is in distinguishing functionally relevant MREs from predicted MREs.

Frequent DNA methylation of MiR-129-2 and its potential clinical implication in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a highly malignant tumor with poor prognosis and high mortality due to a lack of effective medical treatment and apparent early stage symptoms. Understanding molecular mechanism of cancer development is crucial for HCC diagnosis, prognosis, and treatment. Recently, microRNAs have been shown to play an important role in carcinogenesis, being regulated by DNA methylation in several cases. In this study, a whole genome approach was used to identify methylation-regulated miRNAs in HCC, finally focusing on miR-129-2. MiR-129-2 methylation and reduced expression were observed in all examined HCC cell lines but not in normal liver cells and tissues. In 39 (93%) of 42 HCC, the methylation levels of miR-129-2 were significantly increased in tumor tissues compared with adjacent normal tissues. Furthermore, miR-129-2 methylation was detectable in plasma samples from HCC patients, but not in plasma samples from healthy individuals or patients with liver cirrhosis. At a cut-off value of -2.36 (log2 transformation of methylation level), it was possible to distinguish HCC from healthy and cirrhotic controls with sensitivity and specificity of 88% and 100%, respectively. This study indicates that miR-129-2 methylation is highly accurate in distinguishing HCC patients from cirrhosis patients and healthy individuals, implying its potential utility as an early diagnostic marker for HCC.

Prototype of integrated pseudo-dynamic crosstalk network for cancer molecular mechanism.

In this study, we attempted to solve two important challenges in systems biology. First, although the Michaelis-Menten (MM) model provides local kinetic information, it is hard to generalize MM models to model a large system because increasingly large amounts of experimental data are necessary for the parameter identification. In addition, it is not possible to develop an MM model that provides information about the strength of the interactions in the system. Second, although the dynamic simulation of various signal transduction pathways is important in cancer research, it is impossible to theoretically derive a mathematical model to describe the cancer molecular mechanism. Predictive computational approaches can be used to analyze the dynamics of a system and to determine the dysfunction of a regulatory process. In this report, we first propose a pseudo-dynamic pathway to describe protein interactions in an MM system. We then discuss the dynamic behavior of two large-scale systems (antigrowth-signal-induced cell cycle and apoptotic-signal-transduction mechanism). These two systems were constructed through the in-series and organic integration, respectively, of MM modules with Petri net modules; moreover, more than 30% additional reactions were added during this integration step. We then described an extremely large multi-stream system (growth signal transduction); however, the analysis of this system to obtain dynamic predictions is critical but appears impossible. Thus, we introduced a fuzzy concept that can be used to develop a physically realizable model prototype. In the future, through step-by-step in vivo modifications, researchers will be able to develop a complete model of cancer metabolism to achieve accurate predictions.

Adaptive neural-based fuzzy modeling for biological systems.

The inverse problem of identifying dynamic biological networks from their time-course response data set is a cornerstone of systems biology. Hill and Michaelis-Menten model, which is a forward approach, provides local kinetic information. However, repeated modifications and a large amount of experimental data are necessary for the parameter identification. S-system model, which is composed of highly nonlinear differential equations, provides the direct identification of an interactive network. However, the identification of skeletal-network structure is challenging. Moreover, biological systems are always subject to uncertainty and noise. Are there suitable candidates with the potential to deal with noise-contaminated data sets? Fuzzy set theory is developed for handing uncertainty, imprecision and complexity in the real world; for example, we say "driving speed is high" wherein speed is a fuzzy variable and high is a fuzzy set, which uses the membership function to indicate the degree of a element belonging to the set (words in Italics to denote fuzzy variables or fuzzy sets). Neural network possesses good robustness and learning capability. In this study we hybrid these two together into a neural-fuzzy modeling technique. A biological system is formulated to a multi-input-multi-output (MIMO) Takagi-Sugeno (T-S) fuzzy system, which is composed of rule-based linear subsystems. Two kinds of smooth membership functions (MFs), Gaussian and Bell-shaped MFs, are used. The performance of the proposed method is tested with three biological systems.

Plasma proteomic analysis of the critical limb ischemia markers in diabetic patients with hemodialysis.

Critical limb ischemia (CLI) is a severe obstruction of the arteries resulting from seriously decreased blood flow to the extremities, progressing to the point of pain and even skin ulcers or sores. CLI is associated with a high percentage of limb loss and mortality; however, no reliable biochemical indices are available to monitor the stages of CLI. We developed a strategy involving comparative proteomic analysis to detect CLI associated plasma biomarkers. 2D-DIGE and subsequent MALDI-TOF MS analyses provided 50 differentially expressed plasma proteins (including alkaline phosphatase and haptoglobin), between hemodialytic diabetic patients with and without CLI. Interestingly, more than half of the differentially expressed plasma proteins are associated with inflammatory responses. Our results show that CLI is strongly correlated to inflammation, indicating a strong potential for proteomics analysis in the diagnosis of CLI. To the best of our knowledge, this is the first report presenting a proteomics approach to monitor differentially expressed plasma proteins associated with CLI.

Aberrant DNA methylation profile and frequent methylation of KLK10 and OXGR1 genes in hepatocellular carcinoma.

Investigating aberrant DNA methylation in the cancer genome may identify genes that play an important role in tumor progression. In this study, we combined differential methylation hybridization and a CpG microarray platform to characterize methylation profiles and identify novel candidate genes associated with hepatocellular carcinoma (HCC). The genomic DNA of 21 paired adjacent normal and HCC samples was used, and results were analyzed by hierarchical clustering. Twenty-seven hypermethylated candidates and 38 hypomethylated candidates were obtained. Six candidate genes from the hypermethylated group were validated by combined bisulfite restriction analysis; two genes, human kallikrein 10 gene (KLK10) and oxoglutarate (alpha-ketoglutarate) receptor 1 gene (OXGR1), were further analyzed by bisulfite sequencing. The DNA hypermethylation status of KLK10 and OXGR1 were subsequently examined in HCC cell lines and clinical samples using methylation-specific PCR. In 49 HCC samples, 46 (94%) showed that at least one of these two genes was highly methylated. Moreover, KLK10 and OXGR1 mRNA levels were inversely correlated (r = -0.435 and -0.497, P < 0.05) with DNA methylation as examined in paired adjacent normal and tumor samples. Statistical analyses further indicated that KLK10 hypermethylation was significantly associated with cirrhosis (P = 0.042) and HCV infection (P = 0.017) as well as inversely associated with HBV infection (P = 0.023). Furthermore, restoration of KLK10 and OXGR1 expression reduced the ability of anchorage-independent growth, and sensitized HCC cells to doxorubicin- or 5-fluorouracil-induced cytotoxicity. Our results suggest that the hypermethylated KLK10 and OXGR1 are frequent in HCC and may be useful as markers for clinical application.

Computation intelligent for eukaryotic cell-cycle gene network.

Computational intelligent approaches is adopted to construct the S-system of eukaryotic cell cycle for further analysis of genetic regulatory networks. A highly nonlinear power-law differential equation is constructed to describe the transcriptional regulation of gene network from the time-courses dataset. Global artificial algorithm, based on hybrid differential evolution, can achieve global optimization for the highly nonlinear differential gene network modeling. The constructed gene regulatory networks will be a reference for researchers to realize the inhibitory and activatory operator for genes synthesis and decomposition in Eukaryotic cell cycle.

Inference of genetic network of Xenopus frog egg: improved genetic algorithm.

An improved genetic algorithm (IGA) is proposed to achieve S-system gene network modeling of Xenopus frog egg. Via the time-courses training datasets from Michaelis-Menten model, the optimal parameters are learned. The S-system can clearly describe activative and inhibitory interaction between genes as generating and consuming process. We concern the mitotic control in cell-cycle of Xenopus frog egg to realize cyclin-Cdc2 and Cdc25 for MPF activity. The proposed IGA can achieve global search with migration and keep the best chromosome with elitism operation. The generated gene regulatory networks can provide biological researchers for further experiments in Xenopus frog egg cell cycle control.