Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach

The Role of Phytonutrient Kaempferol in the Prevention of Gastrointestinal Cancers: Recent Trends and Future Perspectives

In recent years, kaempferol, a natural flavonoid present in various fruits and vegetables, has received significant attention in gastrointestinal cancer research due to its varied therapeutic effects. Kaempferol has been proven to alter several molecular mechanisms and pathways, such as the PI3/Akt, mTOR, and Erk/MAPK pathway involved in cancer progression, showing its inhibitory effects on cell proliferation, survival, angiogenesis, metastasis, and migration. Kaempferol is processed in the liver and small intestine, but limited bioavailability has been a major concern in the clinical implications of kaempferol. Nano formulations have been proven to enhance kaempferol's efficacy in cancer prevention. The synergy of nanotechnology and kaempferol has shown promising results in in vitro studies, highlighting the importance for more in vivo research and clinical trials to determine safety and efficacy. This review aims to focus on the role of kaempferol in various types of gastrointestinal cancer and how the combination of kaempferol with nanotechnology helps in improving therapeutic efficacy in cancer treatment.

Identification of potential regulatory mechanisms and therapeutic targets for lung cancer

Lung cancer poses a significant health threat globally, especially in regions like India, with 5-year survival rates remain alarmingly low. Our study aimed to uncover key markers for effective treatment and early detection. We identified specific genes related to lung cancer using the BioXpress database and delved into their roles through DAVID enrichment analysis. By employing network theory, we explored the intricate interactions within lung cancer networks, identifying ASPM and MKI67 as crucial regulator genes. Predictions of microRNA and transcription factor interactions provided additional insights. Examining gene expression patterns using GEPIA and KM Plotter revealed the clinical relevance of these key genes. In our pursuit of targeted therapies, Drug Bank pointed to methotrexate as a potential drug for the identified key regulator genes. Confirming this, molecular docking studies through Swiss Dock showed promising binding interactions. To ensure stability, we conducted molecular dynamics simulations using the AMBER 16 suite. In summary, our study pinpoints ASPM and MKI67 as vital regulators in lung cancer networks. The identification of hub genes and functional pathways enhances our understanding of molecular processes, offering potential therapeutic targets. Importantly, methotrexate emerged as a promising drug candidate, supported by robust docking and simulation studies. These findings lay a solid foundation for further experimental validations and hold promise for advancing personalized therapeutic strategies in lung cancer.Communicated by Ramaswamy H. Sarma.

Getting to know ovarian cancer: Focusing on the effect of LncRNAs in this cancer and the effective signaling pathways

This article undertakes a comprehensive investigation of ovarian cancer, examining the complex nature of this challenging disease. The main focus is on understanding the role of long non-coding RNAs (lncRNAs) in the context of ovarian cancer (OC), and their regulatory functions in disease progression. Through extensive research, the article identifies specific lncRNAs that play significant roles in the intricate molecular processes of OC. Furthermore, the study examines the signaling pathways involved in the development of OC, providing a detailed comprehension of the underlying molecular mechanisms. By connecting lncRNA dynamics with signaling pathways, this exploration not only advances our understanding of ovarian cancer but also reveals potential targets for therapeutic interventions. The findings open up opportunities for targeted treatments, highlighting the importance of personalized approaches in addressing this complex disease and driving progress in ovarian cancer research and treatment strategies.

A computational biology approach to identify potential protein biomarkers and drug targets for sporadic amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of upper and lower motor neurons. The sporadic ALS (sALS) is a multigenic disorder and the complex mechanisms underlying its onset are still not fully delineated. Despite the recent scientific advancements, certain aspects of ALS pathogenic targets need to be yet clarified. The aim of the presented study is to identify potential genetic biomarkers and drug targets for sALS, by analysing gene expression profiles, presented in the publicly available GSE68605 dataset, of motor neurons cells obtained from sALS patients. We used different computational approaches including differential expression analysis, protein network mapping, candidate protein biomarker (CPB) identification, elucidation of the role of functional modules, and molecular docking analysis. The resultant top ten up- and downregulated genes were further used to construct protein-protein interaction network (PPIN). The PPIN analysis resulted in identifying four CPBs (namely RIOK2, AKT1, CTNNB1, and TNF) that commonly overlapped with one another in network parameters (degree, bottleneck and maximum neighbourhood component). The RIOK2 protein emerged as a potential mediator of top five functional modules that are associated with RNA binding, lipoprotein particle receptor binding in pre-ribosome, and interferon, cytokine-mediated signaling pathway. Furthermore, molecular docking analysis revealed that cyclosporine exhibited the highest binding affinity (-8.6 kJ/mol) with RIOK2, and surpassed the FDA-approved ALS drugs, such as riluzole and edaravone. This suggested that cyclosporine may serve as a promising candidate for targeting RIOK2 downregulation observed in sALS patients. In order to validate our computational results, it is suggested that in vitro and in vivo studies may be conducted in future to provide a more detailed understanding of ALS diagnosis, prognosis, and therapeutic intervention.

Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators

Introduction: Kinesin family member 5A (KIF5A) is a motor neuron protein expressed in neurons and involved in anterograde transportation of organelles, proteins, and RNA. Variations in the KIF5A gene that interfere with axonal transport have emerged as a distinguishing feature in several neurodegenerative disorders, including hereditary spastic paraplegia (HSP10), Charcot-Marie-Tooth disease type 2 (CMT2), and Amyotrophic Lateral Sclerosis (ALS). Methods: In this study, we implemented a computational structural and systems biology approach to uncover the role of KIF5A in ALS. Using the computational structural biology method, we explored the role of non-synonymous Single Nucleotide Polymorphism (nsSNPs) in KIF5A. Further, to identify the potential inhibitory molecule against the highly destabilizing structure variant, we docked 24 plant-derived phytochemicals involved in ALS. Results: We found KIF5AS291F variant showed the most structure destabilizing behavior and the phytocompound "epigallocatechin gallate" showed the highest binding affinity (-9.0 Kcal/mol) as compared to wild KIF5A (-8.4 Kcal/mol). Further, with the systems biology approach, we constructed the KIF5A protein-protein interaction (PPI) network to identify the associated Kinesin Families (KIFs) proteins, modules, and their function. We also constructed a transcriptional and post-transcriptional regulatory network of KIF5A. With the network topological parameters of PPIN (Degree, Bottleneck, Closeness, and MNC) using CytoHubba and computational knock-out experiment using Network Analyzer, we found KIF1A, 5B, and 5C were the significant proteins. The functional modules were highly enriched with microtubule motor activity, chemical synaptic transmission in neurons, GTP binding, and GABA receptor activity. In regulatory network analysis, we found KIF5A post-transcriptionally down-regulated by miR-107 which is further transcriptionally up-regulated by four TFs (HIF1A, PPARA, SREBF1, and TP53) and down-regulated by three TFs (ZEB1, ZEB2, and LIN28A). Discussion: We concluded our study by finding a crucial variant of KIF5A and its potential therapeutic target (epigallocatechin gallate) and KIF5A associated significant genes with important regulators which could decrypt the novel therapeutics in ALS and other neurodegenerative diseases.

Rare variant burden analysis from exomes of three consanguineous families reveals LILRB1 and PRSS3 as potential key proteins in inflammatory bowel disease pathogenesis

Background

Inflammatory bowel disease (IBD) is a chronic autoimmune disorder characterized by severe inflammation and mucosal destruction of the intestine. The specific, complex molecular processes underlying IBD pathogenesis are not well understood. Therefore, this study is aimed at identifying and uncovering the role of key genetic factors in IBD.

Method

The whole exome sequences (WESs) of three consanguineous Saudi families having many siblings with IBD were analyzed to discover the causal genetic defect. Then, we used a combination of artificial intelligence approaches, such as functional enrichment analysis using immune pathways and a set of computational functional validation tools for gene expression, immune cell expression analyses, phenotype aggregation, and the system biology of innate immunity, to highlight potential IBD genes that play an important role in its pathobiology.

Results

Our findings have shown a causal group of extremely rare variants in the LILRB1 (Q53L, Y99N, W351G, D365A, and Q376H) and PRSS3 (F4L and V25I) genes in IBD-affected siblings. Findings from amino acids in conserved domains, tertiary-level structural deviations, and stability analysis have confirmed that these variants have a negative impact on structural features in the corresponding proteins. Intensive computational structural analysis shows that both genes have very high expression in the gastrointestinal tract and immune organs and are involved in a variety of innate immune system pathways. Since the innate immune system detects microbial infections, any defect in this system could lead to immune functional impairment contributing to IBD.

Conclusion

The present study proposes a novel strategy for unraveling the complex genetic architecture of IBD by integrating WES data of familial cases, with computational analysis.

Systematic analysis to identify novel disease indications and plausible potential chemical leads of glutamate ionotropic receptor NMDA type subunit 1, GRIN1

Schizophrenia is a mental illness affecting the normal lifestyle of adults and early adolescents incurring major symptoms as jumbled speech, involvement in everyday activities eventually got reduced, patients always struggle with attention and memory, reason being both the genetic and environmental factors responsible for altered brain chemistry and structure, resulting in schizophrenia and associated orphan diseases. The network biology describes the interactions among genes/proteins encoding molecular mechanisms of biological processes, development, and diseases. Besides, all the molecular networks, protein-protein Interaction Networks have been significant in distinguishing the pathogenesis of diseases and thereby drug discovery. The present meta-analysis prioritizes novel disease indications viz. rare and orphan diseases associated with target Glutamate Ionotropic Receptor NMDA Type Subunit 1, GRIN1 using text mining knowledge-based tools. Furthermore, ZINC database was virtually screened, and binding conformation of selected compounds was performed and resulted in the identification of Narciclasine (ZINC04097652) and Alvespimycin (ZINC73138787) as potential inhibitors. Furthermore, docked complexes were subjected to MD simulation studies which suggests that the identified leads could be a better potential drug to recuperate schizophrenia.

Troxerutin-Mediated Complement Pathway Inhibition is a Disease-Modifying Treatment for Inflammatory Arthritis

Troxerutin (TXR) is a phytochemical reported to possess anti-inflammatory and hepatoprotective effects. In this study, we aimed to exploit the antiarthritic properties of TXR using an adjuvant-induced arthritic (AIA) rat model. AIA-induced rats showed the highest arthritis score at the disease onset and by oral administration of TXR (50, 100, and 200 mg/kg body weight), reduced to basal level in a dose-dependent manner. Isobaric tags for relative and absolute quantitative (iTRAQ) proteomics tool were employed to identify deregulated joint homogenate proteins in AIA and TXR-treated rats to decipher the probable mechanism of TXR action in arthritis. iTRAQ analysis identified a set of 434 proteins with 65 deregulated proteins (log₂ case/control≥1.5) in AIA. Expressions of a set of important proteins (AAT, T-kininogen, vimentin, desmin, and nucleophosmin) that could classify AIA from the healthy ones were validated using Western blot analysis. The Western blot data corroborated proteomics findings. In silico protein–protein interaction study of tissue-proteome revealed that complement component 9 (C9), the major building blocks of the membrane attack complex (MAC) responsible for sterile inflammation, get perturbed in AIA. Our dosimetry study suggests that a TXR dose of 200 mg/kg body weight for 15 days is sufficient to bring the arthritis score to basal levels in AIA rats. We have shown the importance of TXR as an antiarthritic agent in the AIA model and after additional investigation, its arthritic ameliorating properties could be exploited for clinical usability.

Network medicine in ovarian cancer: topological properties to drug discovery

Network medicine provides network theoretical tools, methods and properties to study underlying laws governing human interactome to identify disease states and disease complexity leading to drug discovery. Within this framework, we investigated the topological properties of ovarian cancer network (OCN) and the roles of hubs to understand OCN organization to address disease states and complexity. The OCN constructed from the experimentally verified genes exhibits fractal nature in the topological properties with deeply rooted functional communities indicating self-organizing behavior. The network properties at all levels of organization obey one parameter scaling law which lacks centrality lethality rule. We showed that $\langle k\rangle $ can be taken as a scaling parameter, where, power law exponent can be estimated from the ratio of network diameters. The betweenness centrality $C_B$ shows two distinct behaviors one shown by high degree hubs and the other by segregated low degree nodes. The $C_B$ power law exponent is found to connect the exponents of distributions of high and low degree nodes. OCN showed the absence of rich-club formation which leads to the missing of a number of attractors in the network causing formation of weakly tied diverse functional modules to keep optimal network efficiency. In OCN, provincial and connector hubs, which includes identified key regulators, take major responsibility to keep the OCN integrity and organization. Further, most of the key regulators are found to be over expressed and positively correlated with immune infiltrates. Finally, few potential drugs are identified related to the key regulators.

Identification of key regulators in Sarcoidosis through multidimensional systems biological approach

Sarcoidosis is a multi-organ disorder where immunology, genetic and environmental factors play a key role in causing Sarcoidosis, but its molecular mechanism remains unclear. Identification of its genetics profiling that regulates the Sarcoidosis network will be one of the main challenges to understand its aetiology. We have identified differentially expressed genes (DEGs) by analyzing the gene expression profiling of Sarcoidosis and compared it with healthy control. Gene set enrichment analysis showed that these DEGs were mainly enriched in the inflammatory response, immune system, and pathways in cancer. Sarcoidosis protein interaction network was constructed by a total of 877 DEGs (up-down) and calculated its network topological properties, which follow hierarchical scale-free fractal nature up to six levels of the organization. We identified a large number of leading hubs that contain six key regulators (KRs) including ICOS, CTLA4, FLT3LG, CD33, GPR29 and ITGA4 are deeply rooted in the network from top to bottom, considering a backbone of the network. We identified the transcriptional factors (TFs) which are closely interacted with KRs. These genes and their TFs regulating the Sarcoidosis network are expected to be the main target for the therapeutic approaches and potential biomarkers. However, experimental validations of KRs needed to confirm their efficacy.

A Bioinformatics Approach to Identifying Potential Biomarkers for Cryptosporidium parvum: A Coccidian Parasite Associated with Fetal Diarrhea

Cryptosporidium parvum (C. parvum) is a protozoan parasite known for cryptosporidiosis in pre-weaned calves. Animals and patients with immunosuppression are at risk of developing the disease, which can cause potentially fatal diarrhoea. The present study aimed to construct a network biology framework based on the differentially expressed genes (DEGs) of C. parvum infected subjects. In this way, the gene expression profiling analysis of C. parvum infected individuals can give us a snapshot of actively expressed genes and transcripts under infection conditions. In the present study, we have analyzed microarray data sets and compared the gene expression profiles of the patients with the different data sets of the healthy control. Using a network medicine approach to identify the most influential genes in the gene interaction network, we uncovered essential genes and pathways related to C. parvum infection. We identified 164 differentially expressed genes (109 up- and 54 down-regulated DEGs) and allocated them to pathway and gene set enrichment analysis. The results underpin the identification of seven significant hub genes with high centrality values: ISG15, MX1, IFI44L, STAT1, IFIT1, OAS1, IFIT3, RSAD2, IFITM1, and IFI44. These genes are associated with diverse biological processes not limited to host interaction, type 1 interferon production, or response to IL-gamma. Furthermore, four genes (IFI44, IFIT3, IFITM1, and MX1) were also discovered to be involved in innate immunity, inflammation, apoptosis, phosphorylation, cell proliferation, and cell signaling. In conclusion, these results reinforce the development and implementation of tools based on gene profiles to identify and treat Cryptosporidium parvum-related diseases at an early stage.

Antineoplastic action of sulforaphane on HeLa cells by modulation of signaling pathways and epigenetic pathways

BACKGROUND

Epigenetic modifications alter signaling and molecular pathways and are an important therapeutic target. This study examined the effect of sulforaphane on molecular targets in HeLa cells.

METHODS

Quantitative PCR of various molecular targets was performed. Activity of epigenetic enzymes was measured by ELISA and molecular docking analysis was conducted. Promoter methylation of some tumour suppressor genes was quantified using PCR based methylation array. In-silico protein-protein interaction network analysis was performed to understand the effect of transcriptional changes.

RESULTS

Quantitative PCR demonstrated the transcriptional modulation of genes involved in proliferation, metastasis, inflammation, signal transduction pathways and chromatin modifiers. Sulforaphane reduced the enzymatic activity of DNA methyl transferases, histone deacetylases and histone methyltransferases. Molecular docking results suggest that sulforaphane competitively inhibited several DNA methyl transferases and histone deacetylases. Promoter 5'CpG methylation levels of selected tumour suppressor genes was found to be reduced which correlated with their transcriptional increase as well modulation of epigenetic enzymes. Further, protein-protein interaction network analysis discerned the participation of genes towards cancer pathways. Functional enrichment and pathway-based analysis represented the modulation of epigenetic and signaling pathways on sulforaphane treatment.

CONCLUSIONS

The modulation in transcriptional status of epigenetic regulators, genes involved in tumorigenesis resulting in tumour suppressor genes demethylation and re-expression underscores the mechanism behind the anti-cancer effect of sulforaphane on HeLa cells.

Identification of the Key Regulators of Spina Bifida Through Graph-Theoretical Approach

Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

Identification of key regulators in parathyroid adenoma using an integrative gene network analysis

Parathyroid adenoma (PA) is marked by a certain benign outgrowth in the surface of parathyroid glands. The transcriptome analysis of parathyroid adenomas can provide a deep insight into actively expressed genes and transcripts. Hence, we analyzed and compared the gene expression profiles of parathyroid adenomas and healthy parathyroid gland tissues from Gene Expression Omnibus (GEO) database. We identified a total of 280 differentially expressed genes (196 up-regulated, 84 down-regulated), which are involved in a wide array of biological processes. We further constructed a gene interaction network and analyzed its topological properties to know the network structure and its hidden mechanism. This will help to understand the molecular mechanisms underlying parathyroid adenoma development. We thus identified 13 key regulators (PRPF19, SMC3, POSTN, SNIP1, EBF1, MEIS2, PAX9, SCUBE2, WNT4, ARHGAP10, DOCK5, CAV1 and VSIR), which are deep-rooted from top to bottom in the gene interaction network forming a backbone for the network. The structural features of the network are probably maintained by crosstalk between important genes within the network along with associated functional modules.Thus, gene-expression profiling and network approach could be used to provide an independent platform to glen insights from available clinical data.

LncRNAs in Ovarian Cancer Progression, Metastasis, and Main Pathways: ceRNA and Alternative Mechanisms

Ovarian cancer (OvCa) develops asymptomatically until it reaches the advanced stages with metastasis, chemoresistance, and poor prognosis. Our review focuses on the analysis of regulatory long non-coding RNAs (lncRNAs) competing with protein-coding mRNAs for binding to miRNAs according to the model of competitive endogenous RNA (ceRNA) in OvCa. Analysis of publications showed that most lncRNAs acting as ceRNAs participate in OvCa progression: migration, invasion, epithelial-mesenchymal transition (EMT), and metastasis. More than 30 lncRNAs turned out to be predictors of survival and/or response to therapy in patients with OvCa. For a number of oncogenic (CCAT1, HOTAIR, NEAT1, and TUG1 among others) and some suppressive lncRNAs, several lncRNA/miRNA/mRNA axes were identified, which revealed various functions for each of them. Our review also considers examples of alternative mechanisms of actions for lncRNAs besides being ceRNAs, including binding directly to mRNA or protein, and some of them (DANCR, GAS5, MALAT1, and UCA1 among others) act by both mechanisms depending on the target protein. A systematic analysis based on the data from literature and Panther or KEGG (Kyoto Encyclopedia of Genes and Genomes) databases showed that a significant part of lncRNAs affects the key pathways involved in OvCa metastasis, EMT, and chemoresistance.