Association of HOTAIR rs7958904 Polymorphism with Cervical Cancer Risk.

Cervical cancer (CC) has been the prominent cause of cancer-associated fatalities among women in developing countries. In terms of occurrence and mortality, it is ranked second in Bangladesh. Although different genetic polymorphisms linked with this cancer have been investigated over time, the association between the HOTAIR rs7958904 variant and cervical cancer is being reported for the first time in Bangladeshi women. RT-PCR-based TaqMan assay was employed to perform this case-control study on 200 cervical cancer patients and 148 healthy volunteers. Both cases and controls had average ages of 57.5 and 52.5 years, respectively. According to Hardy-Weinberg equilibrium, the rs7958904 allele of HOTAIR gene pretended no deviation for both cases and control groups. The genotyping results showed that rs7958904 has a significant correlation to the development of cervical cancer in different genetic association models, such as co-dominant 1 (CC vs. GG: OR = 1.67, p = 0.0435), co-dominant 2 (CC vs. GG: OR = 3.13, p = 0.0006), co-dominant 3 (CC vs. CG: OR = 1.88, p = 0.0384), dominant (CG + CC vs. GG: OR = 1.98, p = 0.004), recessive (CC vs. GG + CG: OR = 2.25, p = 0.005), and allele model (C vs. G: OR = 1.70, p = 0.0006). In conclusion, the HOTAIR rs7958904 variant has a substantial role in cervical cancer development in Bangladeshi women. Further functional studies with a larger population size are required to support our findings.

Deciphering KDM8 dysregulation and CpG methylation in hepatocellular carcinoma using multi-omics and machine learning.

Aim: This study investigates the altered expression and CpG methylation patterns of histone demethylase KDM8 in hepatocellular carcinoma (HCC), aiming to uncover insights and promising diagnostics biomarkers.Materials & methods: Leveraging TCGA-LIHC multi-omics data, we employed R/Bioconductor libraries and Cytoscape to analyze and construct a gene correlation network, and LASSO regression to develop an HCC-predictive model.Results: In HCC, KDM8 downregulation is correlated with CpGs hypermethylation. Differential gene correlation analysis unveiled a liver carcinoma-associated network marked by increased cell division and compromised liver-specific functions. The LASSO regression identified a highly accurate HCC prediction signature, prominently featuring CpG methylation at cg02871891.Conclusion: Our study uncovers CpG hypermethylation at cg02871891, possibly influencing KDM8 downregulation in HCC, suggesting these as promising biomarkers and targets.

Changes in gene function can play a role in causing cancer. In this study, we looked at how a specific gene called KDM8 behaves in liver cancer. By analyzing a large set of liver cancer samples, we investigated how gene interactions are different in this disease and if they can help predict liver cancer risk. Our results show that the KDM8 gene is less active, and its DNA gets chemically modified more often in liver cancer. We also found a group of genes and DNA changes, which are linked to the disease. Using this information, we identified 16 important markers and built a computer model that can accurately predict liver cancer. We found that DNA methylation at a specific spot called cg02871891 is especially important for predicting liver cancer. Overall, our study suggests that high levels of DNA methylation may lead to reduced KDM8 activity in liver cancer, which could be important for future research and better diagnostic tools.

In Silico Functional Characterization of a Hypothetical Protein From Pasteurella Multocida Reveals a Novel S-Adenosylmethionine-Dependent Methyltransferase Activity.

Genomes may now be sequenced in a matter of weeks, leading to an influx of "hypothetical" proteins (HP) whose activities remain a mystery in GenBank. The information included inside these genes has quickly grown in prominence. Thus, we selected to look closely at the structure and function of an HP (AFF25514.1; 246 residues) from Pasteurella multocida (PM) subsp. multocida str. HN06. Possible insights into bacterial adaptation to new environments and metabolic changes might be gained by studying the functions of this protein. The PM HN06 2293 gene encodes an alkaline cytoplasmic protein with a molecular weight of 28352.60 Da, an isoelectric point (pI) of 9.18, and an overall average hydropathicity of around -0.565. One of its functional domains, tRNA (adenine (37)-N6)-methyltransferase TrmO, is a S-adenosylmethionine (SAM)-dependent methyltransferase (MTase), suggesting that it belongs to the Class VIII SAM-dependent MTase family. The tertiary structures represented by HHpred and I-TASSER models were found to be flawless. We predicted the model's active site using the Computed Atlas of Surface Topography of Proteins (CASTp) and FTSite servers, and then displayed it in 3 dimensional (3D) using PyMOL and BIOVIA Discovery Studio. Based on molecular docking (MD) results, we know that HP interacts with SAM and S-adenosylhomocysteine (SAH), 2 crucial metabolites in the tRNA methylation process, with binding affinities of 7.4 and 7.5 kcal/mol, respectively. Molecular dynamic simulations (MDS) of the docked complex, which included only modest structural adjustments, corroborated the strong binding affinity of SAM and SAH to the HP. Evidence for HP's possible role as an SAM-dependent MTase was therefore given by the findings of Multiple sequence alignment (MSA), MD, and molecular dynamic modeling. These in silico data suggest that the investigated HP might be used as a useful adjunct in the investigation of Pasteurella infections and the development of drugs to treat zoonotic pasteurellosis.

Computational insights into the stereo-selectivity of catechins for the inhibition of the cancer therapeutic target EGFR kinase.

The epidermal growth factor receptor (EGFR) plays a crucial role in regulating cellular growth and survival, and its dysregulation is implicated in various cancers, making it a prime target for cancer therapy. Natural compounds known as catechins have garnered attention as promising anticancer agents. These compounds exert their anticancer effects through diverse mechanisms, primarily by inhibiting receptor tyrosine kinases (RTKs), a protein family that includes the notable member EGFR. Catechins, characterized by two chiral centers and stereoisomerism, demonstrate variations in chemical and physical properties due to differences in the spatial orientation of atoms. Although previous studies have explored the membrane fluidity effects and transport across cellular membranes, the stereo-selectivity of catechins concerning EGFR kinase inhibition remains unexplored. In this study, we investigated the stereo-selectivity of catechins in inhibiting EGFR kinase, both in its wild-type and in the prevalent L858R mutant. Computational analyses indicated that all stereoisomers, including the extensively studied catechin (-)-EGCG, effectively bound within the ATP-binding site, potentially inhibiting EGFR kinase activity. Notably, gallated catechins emerged as superior EGFR inhibitors to their non-gallated counterparts, revealing intriguing binding trends. The top four stereoisomers exhibiting high dock scores and binding energies with wild-type EGFR comprise (-)-CG (-)-GCG (+)-CG, and (-)-EGCG. To assess dynamic behavior and stability, molecular dynamics simulations over 100 ns were conducted for the top-ranked catechin (-)-CG and the widely investigated catechin (-)-EGCG with EGFR kinase. This study enhances our understanding of how the stereoisomeric nature of a drug influences inhibitory potential, providing insights that could guide the selection of specific stereoisomers for improved efficacy inexisting drugs.

A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer.

The lack of precise molecular signatures limits the early diagnosis of non-small cell lung cancer (NSCLC). The present study used gene expression data and interaction networks to develop a highly accurate model with the least absolute shrinkage and selection operator (LASSO) for predicting NSCLC. The differentially expressed genes (DEGs) were identified in NSCLC compared with normal tissues using TCGA and GTEx data. A biological network was constructed using DEGs, and the top 20 upregulated and 20 downregulated hub genes were identified. These hub genes were used to identify signature genes with penalized logistic regression using the LASSO to predict NSCLC. Our model's development involved the following steps: (i) the dataset was divided into 80% for training (TR) and 20% for testing (TD1); (ii) a LASSO logistic regression analysis was performed on the TR with 10-fold cross-validation and identified a combination of 17 genes as NSCLC predictors, which were used further for development of the LASSO model. The model's performance was assessed on the TD1 dataset and achieved an accuracy and an area under the curve of the receiver operating characteristics (AUC-ROC) of 0.986 and 0.998, respectively. Furthermore, the performance of the LASSO model was evaluated using three independent NSCLC test datasets (GSE18842, GSE27262, GSE19804) and achieved high accuracy, with an AUC-ROC of >0.99, >0.99, and 0.95, respectively. Based on this study, a web application called NSCLCpred was developed to predict NSCLC.

Molecular approaches in cancer.

Cancer remains the second leading cause of death worldwide and newly diagnosed cases have increased at an alarming rate. One in every four people has a lifetime risk of being afflicted with cancer. Early diagnosis, which is essential in reducing morbidity and mortality, requires the development of highly sensitive and specific techniques to identify and monitor molecular changes for cancer-specific genetic and epigenetic markers. Among these, fluorescent in situ hybridization (FISH), Polymerase Chain Reaction (PCR), DNA microarray and NanoString technologies are notable. Recent advances in the development of efficient and cost-effective next-generation sequencing (NGS) has enabled whole genome, exome and transcriptome analysis. This review focuses on the features and applications of important molecular techniques to detect various genetic mutations thus enabling improved diagnosis, treatment and outcome.

Aspirin attenuates the expression of adhesion molecules, risk of obesity, and adipose tissue inflammation in high-fat diet-induced obese mice.

The prevalence of obesity is increasing at an alarming rate and keeps on being one of the significant challenges of this century. Obesity promotes adipose tissue hypertrophy and causes the release of different pro-inflammatory cytokines, playing a significant role in the pathophysiology of metabolic syndrome. Aspirin is known as a potent anti-inflammatory drug, but its role in adipogenesis, adipocyte-specific inflammation, and metabolic syndrome is not well characterized. Thus, in this experiment, we aimed to determine the effect of low-dose aspirin on obesity, obesity-induced inflammation, and metabolic syndrome. High-fat diet-induced obese female mice (Swiss Albino) were used in our study. Mice were fed on a normal diet, a high-fat diet, and a low dose of aspirin (LDA) in the presence of a high-fat diet for 11 weeks. Body weight, lipid profile, adipose tissue size, and inflammatory status were analyzed after that period. The ∆∆CT method was used to calculate the relative mRNA expression of target genes. Treatment with a low dose of aspirin resulted in a significant reduction of body weight, visceral fat mass and serum total cholesterols, serum and adipose tissue triglycerides, and blood glucose levels in high-fat diet-induced obese mice compared to the untreated obese group. Consistent with these biochemical results, a significant reduction in mRNA expression of different genes like PPARγ, GLUT4, IL-6, TNFα, MCP-1, ICAM-I, and VCAM-I associated with adipogenesis and inflammation were noticed. Overall, current study findings indicate that low-dose aspirin reduces obesity, hyperlipidemia, adipocyte-specific inflammation, and metabolic syndrome in high-fat diet-induced obese mice.

The Histone H3K27me3 Demethylases KDM6A/B Resist Anoikis and Transcriptionally Regulate Stemness-Related Genes.

Epithelial cancer cells that lose attachment from the extracellular matrix (ECM) to seed in a distant organ often undergo anoikis's specialized form of apoptosis. Recently, KDM3A (H3K9 demethylase) has been identified as a critical effector of anoikis in cancer cells. However, whether other histone demethylases are involved in promoting or resisting anoikis remains elusive. We screened the major histone demethylases and found that both H3K27 histone demethylases, namely, KDM6A/B were highly expressed during ECM detachment. Inhibition of the KDM6A/B activity by using a specific inhibitor results in reduced sphere formation capacity and increased apoptosis. Knockout of KDM6B leads to the loss of stem cell properties in solitary cells. Furthermore, we found that KDM6B maintains stemness by transcriptionally regulating the expression of stemness genes SOX2, SOX9, and CD44 in detached cells. KDM6B occupies the promoter region of both SOX2 and CD44 to regulate their expression epigenetically. We also noticed an increased occupancy of the HIF1α promoter by KDM6B, suggesting its regulatory role in maintaining hypoxia in detached cancer cells. This observation was further strengthened as we found a significant positive association in the expression of both KDM6B and HIF1α in various cancer types. Overall, our results reveal a novel transcriptional program that regulates resistance against anoikis and maintains stemness-like properties.

A Computational Approach Identified Andrographolide as a Potential Drug for Suppressing COVID-19-Induced Cytokine Storm.

BACKGROUND: The newly identified betacoronavirus SARS-CoV-2 is the causative pathogen of the coronavirus disease of 2019 (COVID-19) that killed more than 3.5 million people till now. The cytokine storm induced in severe COVID-19 patients causes hyper-inflammation, is the primary reason for respiratory and multi-organ failure and fatality. This work uses a rational computational strategy to identify the existing drug molecules to target host pathways to reduce the cytokine storm. RESULTS: We used a "host response signature network" consist of 36 genes induced by SARS-CoV-2 infection and associated with cytokine storm. In order to attenuate the cytokine storm, potential drug molecules were searched against "host response signature network". Our study identified that drug molecule andrographolide, naturally present in a medicinal plant Andrographis paniculata, has the potential to bind with crucial proteins to block the TNF-induced NFkB1 signaling pathway responsible for cytokine storm in COVID-19 patients. The molecular docking method showed the binding of andrographolide with TNF and covalent binding with NFkB1 proteins of the TNF signaling pathway. CONCLUSION: We used a rational computational approach to repurpose existing drugs targeting host immunomodulating pathways. Our study suggests that andrographolide could bind with TNF and NFkB1 proteins, block TNF-induced cytokine storm in COVID-19 patients, and warrant further experimental validation.

pssRNAit: A Web Server for Designing Effective and Specific Plant siRNAs with Genome-Wide Off-Target Assessment.

We report an advanced web server, the plant-specific small noncoding RNA interference tool pssRNAit, which can be used to design a pool of small interfering RNAs (siRNAs) for highly effective, specific, and nontoxic gene silencing in plants. In developing this tool, we integrated the transcript dataset of plants, several rules governing gene silencing, and a series of computational models of the biological mechanism of the RNA interference (RNAi) pathway. The designed pool of siRNAs can be used to construct a long double-strand RNA and expressed through virus-induced gene silencing (VIGS) or synthetic transacting siRNA vectors for gene silencing. We demonstrated the performance of pssRNAit by designing and expressing the VIGS constructs to silence Phytoene desaturase (PDS) or a ribosomal protein-encoding gene, RPL10 (QM), in Nicotiana benthamiana We analyzed the expression levels of predicted intended-target and off-target genes using reverse transcription quantitative PCR. We further conducted an RNA-sequencing-based transcriptome analysis to assess genome-wide off-target gene silencing triggered by the fragments that were designed by pssRNAit, targeting different homologous regions of the PDS gene. Our analyses confirmed the high accuracy of siRNA constructs designed using pssRNAit The pssRNAit server, freely available at https://plantgrn.noble.org/pssRNAit/, supports the design of highly effective and specific RNAi, VIGS, or synthetic transacting siRNA constructs for high-throughput functional genomics and trait improvement in >160 plant species.