Reconstruction and visualization of carbohydrate, N-glycosylation pathways in Pichia pastoris CBS7435 using computational and system biology approaches

Impact of Healthcare on Stock Market Volatility and Its Predictive Solution Using Improved Neural Network

The unprecedented Corona Virus Disease (COVID-19) pandemic has put the world in peril and shifted global landscape in unanticipated ways. The SARSCoV2 virus, which caused the COVID-19 outbreak, first appeared in Wuhan, Hubei Province, China, in December 2019 and quickly spread around the world. This pandemic is not only a global health crisis, but it has caused the major global economic depression. As soon as the virus spread, stock market prices plummeted and volatility increased. Predicting the market during this outbreak has been of substantial importance and is the primary motivation to carry out this work. Given the nonlinearity and dynamic nature of stock data, the prediction of stock market is a challenging task. The machine learning models have proven to be a good choice for the development of effective and efficient prediction systems. In recent years, the application of hyperparameter optimization techniques for the development of highly accurate models has increased significantly. In this study, a customized neural network model is proposed and the power of hyperparameter optimization in modelling stock index prices is explored. A novel dataset is generated using nine standard technical indicators and COVID-19 data. In addition, the primary focus is on the importance of selection of optimal features and their preprocessing. The utilization of multiple feature ranking techniques combined with extensive hyperparameter optimization procedures is comprehensive for the prediction of stock index prices. Moreover, the model is evaluated by comparing it with other models, and results indicate that the proposed model outperforms other models. Given the detailed design methodology, preprocessing, exploratory feature analysis, and hyperparameter optimization procedures, this work gives a significant contribution to stock analysis research community during this pandemic.

Genistein Modulates Signaling Pathways and Targets Several Epigenetic Markers in HeLa Cells

BACKGROUND

Several epigenetic changes are responsible for transcriptional alterations of signaling pathways and tumour suppressor genes (TSGs) contributing to carcinogenesis. This study was aimed to examine the effect of the phytochemical, genistein on various molecular targets in HeLa cells.

METHODS

Quantitative PCR was used to analyze the expression of various molecular targets. Biochemical assays were employed to study the epigenetic enzymes. To correlate the transcriptional status of the selected TSGs and epigenetic modulation, their promoter 5'CpG methylation levels were evaluated by quantitative methylation array followed by methylation specific restriction digestion.

RESULTS

The expression of several genes involved in the cell cycle regulation, migration, inflammation, phosphatidylinositol 3-kinase (PI3K) and mitogen activated kinase-like protein (MAPK) pathway were found to be modulated including CCNB1, TWIST1, MMP14, TERT, AKT1, PTPRR, FOS and IL1A. Genistein modulated the expression of DNA methyltransferases (DNMTs), histone deacetylases (HDACs), histone methyltransferases (HMTs), demethylases, and histone phosphorylases. Furthermore, genistein decreased the activity of DNMTs, HDACs, and HMTs and reduced global DNA methylation levels. Promoter methylation of several TSGs, including FHIT, RUNX3, CDH1, PTEN, and SOC51, was lowered with corresponding transcriptional increase. Network analysis indicated similar effect of genistein.

CONCLUSION

This study presents a comprehensive mechanism of action of genistein showcasing effective epigenetic modulation and widespread transcriptional changes resulting in restoration of tumour suppressor gene expression. This study corroborates the development of genistein as a candidate for anti-cancer therapy.

Inhibition of C298S mutant of human aldose reductase for antidiabetic applications: Evidence from in silico elementary mode analysis of biological network model

Human aldose reductase (hAR) is the key enzyme in sorbitol pathway of glucose utilization and is implicated in the etiology of secondary complications of diabetes, such as, cardiovascular complications, neuropathy, nephropathy, retinopathy, and cataract genesis. It reduces glucose to sorbitol in the presence of NADPH and the major cause of diabetes complications could be the change in the osmotic pressure due to the accumulation of sorbitol. An activated form of hAR (activated hAR or ahAR) poses a potential obstacle in the development of diabetes drugs as hAR-inhibitors are ineffective against ahAR. The therapeutic efficacy of such drugs is compromised when a large fraction of the enzyme (hAR) undergoes conversion to the activated ahAR form as has been observed in the diabetic tissues. In the present study, attempts have been made to employ systems biology strategies to identify the elementary nodes of human polyol metabolic pathway, responsible for normal metabolic states, followed by the identification of natural potent inhibitors of the activated form of hAR represented by the mutant C298S for possible antidiabetic applications. Quantum Mechanical Molecular Mechanical docking strategy was used to determine the probable inhibitors of ahAR. Rosmarinic acid was found as the most potent natural ahAR inhibitor and warrants for experimental validation in the near future.

Effect of N-linked glycosylation modification of pro-Met on function of c-Met

AIM: To investigate the effect of N-linked glycosylation modification of pro-Met on the function of c-Met in human hepatocellular carcinoma (HCC) cell line MHCC-97H and human cholangiocarcinoma cell line RBE.

METHODS: Tunicamycin (Tun), an inhibitor of N-glycosylation, MG132, a proteasome inhibitor, cycloheximide (CHX), a protein synthesis inhibitor, and PF-2341066, a c-Met inhibitor, were used in this study. The effect of N-linked glycosylation modification of pro-Met on c-Met function in MHCC-97H and RBE cells were determined by Western blot, immunofluorescence and confocal microscopy.

RESULTS: We found that both phosphatidylinositol 3 kinase/Akt (PI3K/Akt) and mitogen-activated protein kinase /extracellular signal-regulated kinase (MEK/ERK) were downstream to c-Met. Furthermore, c-Met was an N-linked glycosylated protein. N-glycosylation modification of p145Met was from pro-Met, but not due to the further modification of pro-Met. Importantly, blocking the N-glycosylation of pro-Met targeted pro-Met to the cytoplasm and initiated its phosphorylation independent of hepatocyte growth factor (HGF) engagement. Nonglycosylated pro-Met, which was phosphorylated, only in part activated c-Met downstream signals.

CONCLUSION: N-glycosylation is required for c-Met function.