Regulation of anxiety-like behaviors by S-palmitoylation and S-nitrosylation in basolateral amygdala.

Protein posttranslational modification regulates synaptic protein stability, sorting and trafficking, and is involved in emotional disorders. Yet the molecular mechanisms regulating emotional disorders remain unelucidated. Here we report unknown roles of protein palmitoylation/nitrosylation crosstalk in regulating anxiety-like behaviors in rats.

Honeysuckle () and Huangqi () Suppress SARS-CoV-2 Entry and COVID-19 Related Cytokine Storm .

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.