Methods in DNA methylation array dataset analysis: A review.

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

Insights into dietary phytochemicals targeting Parkinson's disease key genes and pathways: A network pharmacology approach.

Parkinson's disease (PD) is a complex neurological disease associated with the degeneration of dopaminergic neurons. Oxidative stress is a key player in instigating apoptosis in dopaminergic neurons. To improve the survival of neurons many dietary phytochemicals have gathered significant attention recently. Thus, the present study implements a comprehensive network pharmacology approach to unravel the mechanisms of action of dietary phytochemicals that benefit disease management. A literature search was performed to identify ligands (i.e., comprising dietary phytochemicals and Food and Drug Administration pre-approved PD drugs) in the PubMed database. Targets associated with selected ligands were extracted from the search tool for interactions of chemicals (STITCH) database. Then, the construction of a gene-gene interaction (GGI) network, analysis of hub-gene, functional and pathway enrichment, associated transcription factors, miRNAs, ligand-target interaction network, docking were performed using various bioinformatics tools together with molecular dynamics (MD) simulations. The database search resulted in 69 ligands and 144 unique targets. GGI and subsequent topological measures indicate histone acetyltransferase p300 (EP300), mitogen-activated protein kinase 1 (MAPK1) or extracellular signal-regulated kinase (ERK)2, and CREB-binding protein (CREBBP) as hub genes. Neurodegeneration, MAPK signaling, apoptosis, and zinc binding are key pathways and gene ontology terms. hsa-miR-5692a and SCNA gene-associated transcription factors interact with all the 3 hub genes. Ligand-target interaction (LTI) network analysis suggest rasagiline and baicalein as candidate ligands targeting MAPK1. Rasagiline and baicalein form stable complexes with the Y205, K330, and V173 residues of MAPK1. Computational molecular insights suggest that baicalein and rasagiline are promising preclinical candidates for PD management.

Systems and Computational Screening identifies SRC and NKIRAS2 as Baseline Correlates of Risk (CoR) for Live Attenuated Oral Typhoid Vaccine (TY21a) associated Protection.

AIM: We investigated the molecular underpinnings of variation in immune responses to the live attenuated typhoid vaccine (Ty21a) by analyzing the baseline immunological profile. We utilized gene expression datasets obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE100665) before and after immunization. We then employed two distinct computational approaches to identify potential baseline biomarkers associated with responsiveness to the Ty21a vaccine. MAIN METHODS: The first pipeline (knowledge-based) involved the retrieval of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network construction, and topological network analysis of post-immunization datasets before gauging their pre-vaccination expression levels. The second pipeline utilized an unsupervised machine learning algorithm for data-driven feature selection on pre-immunization datasets. Supervised machine-learning classifiers were employed to computationally validate the identified biomarkers. KEY FINDINGS: Baseline activation of NKIRAS2 (a negative regulator of NF-kB signalling) and SRC (an adaptor for immune receptor activation) was negatively associated with Ty21a vaccine responsiveness, whereas LOC100134365 exhibited a positive association. The Stochastic Gradient Descent (SGD) algorithm accurately distinguished vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1% accuracy for the three identified genes, respectively. SIGNIFICANCE: This dual-pronged novel analytical approach provides a comprehensive comparison between knowledge-based and data-driven methods for the prediction of baseline biomarkers associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the needle further towards the need for development of precise enteric vaccines and on the importance of pre-immunization screening.

Nanomaterials-assisted photothermal therapy for breast cancer: State-of-the-art advances and future perspectives.

Breast cancer (BC) remains an enigmatic fatal modality ubiquitously prevalent in different parts of the world. Contemporary medicines face severe challenges in remediating and healing breast cancer. Due to its spatial specificity and nominal invasive therapeutic regime, photothermal therapy (PTT) has attracted much scientific attention down the lane. PTT utilizes a near-infrared (NIR) light source to irradiate the tumor target intravenously or non-invasively, which is converted into heat energy over an optical fibre. Dynamic progress in nanomaterial synthesis was achieved with specialized visual, physicochemical, biological, and pharmacological features to make up for the inadequacies and expand the horizon of PTT. Numerous nanomaterials have substantial NIR absorption and can function as efficient photothermal transducers. It is achievable to limit the wavelength range of an absorbance peak for specific nanomaterials by manipulating their synthesis, enhancing the precision and quality of PTT. Along the same lines, various nanomaterials are conjugated with a wide range of surface-modifying chemicals, including polymers and antibodies, which may modify the persistence of the nanomaterial and diminish toxicity concerns. In this article, we tend to put forth specific insights and fundamental conceptualizations on pre-existing PTT and its advances upon conjugation with different biocompatible nanomaterials working in synergy to combat breast cancer, encompassing several strategies like immunotherapy, chemotherapy, photodynamic therapy, and radiotherapy coupled with PTT. Additionally, the role or mechanisms of nanoparticles, as well as possible alternatives to PTT, are summarized as a distinctive integral aspect in this article.

Exploring the theranostic potentials of miRNA and epigenetic networks in autoimmune diseases: A comprehensive review.

BACKGROUND: Autoimmune diseases (AD) are severe pathophysiological ailments that are stimulated by an exaggerated immunogenic response towards self-antigens, which can cause systemic or site-specific organ damage. An array of complex genetic and epigenetic facets majorly contributes to the progression of AD, thus providing significant insight into the regulatory mechanism of microRNA (miRNA). miRNAs are short, non-coding RNAs that have been identified as essential contributors to the post-transcriptional regulation of host genome expression and as crucial regulators of a myriad of biological processes such as immune homeostasis, T helper cell differentiation, central and peripheral tolerance, and immune cell development. AIMS: This article tends to deliberate and conceptualize the brief pathogenesis and pertinent epigenetic regulatory mechanism as well as miRNA networks majorly affecting five different ADs namely rheumatoid arthritis (RA), type 1 diabetes, multiple sclerosis (MS), systemic lupus erythematosus (SLE) and inflammatory bowel disorder (IBD) thereby providing novel miRNA-based theranostic interventions. RESULTS & DISCUSSION: Pertaining to the differential expression of miRNA attributed in target tissues and cellular bodies of innate and adaptive immunity, a paradigm of scientific expeditions suggests an optimistic correlation between immunogenic dysfunction and miRNA alterations. CONCLUSION: Therefore, it is not astonishing that dysregulations in miRNA expression patterns are now recognized in a wide spectrum of disorders, establishing themselves as potential biomarkers and therapeutic targets. Owing to its theranostic potencies, miRNA targets have been widely utilized in the development of biosensors and other therapeutic molecules originating from the same.

Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer's disease.

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease and a leading cause of dementia. We aim to identify key genes for the development of therapeutic targets and biomarkers for potential treatments for AD. Meta-analysis was performed on six microarray datasets and identified the differentially expressed genes between healthy and Alzheimer's disease samples. Thereafter, we filtered out the common genes which were present in at least four microarray datasets for downstream analysis. We have constructed a gene-gene network for the common genes and identified six hub genes. Furthermore, we investigated the regulatory mechanisms of these hub genes by analysing their interaction with miRNAs and transcription factors. The gene ontology analysis results highlighted the enriched terms significantly associated with hub genes. Through an extensive literature survey, we found that three of the hub genes including GRIN1, SYN2, and SYT1 were critically involved in disease development. To leverage existing drugs for potential repurposing, we predicted drug-gene interaction using the drug-gene interaction database, and performed molecular docking studies. The docking results revealed that the drug compounds had strong interactions and favorable binding with selected hub genes. Lorazepam exhibits a binding energy of -7.3 kcal/mol with GRIN1, Lorediplon exhibits binding energies of -7.7 kcal/mol and -6.3 kcal/mol with the SYT1, and SYN2 respectively. In addition, 100 ns molecular dynamics simulations were carried out for the top complexes and apo protein as well. Furthermore, the MM-PBSA free energy calculations also revealed that these complexes are stable and had favorable energies. According to our study, the identified hub gene could serve as a biomarker as well as a therapeutic target for AD, and the proposed repurposed drug molecules appear to have promising efficacy in treating the disease.Communicated by Ramaswamy H. Sarma.

Targeting NF-κB pathway for the anti-inflammatory potential of Bhadradarvadi kashayam on stimulated RAW 264.7 macrophages.

Macrophage-arbitrated inflammation is associated with the regulation of rheumatoid arthritis (RA). Low risk and better efficiency are steered herbal drugs more credible than conventional medicines in RA management. Bhadradarvadi (BDK) concoction has been traditionally used for rheumatism in Ayurveda. However, the mechanisms at the molecular level are still elusive. This study was designed to inspect the process of immunomodulation and anti-inflammatory properties of BDK in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages for the first time. BDK concoction was prepared and evaluated with the stimulated murine macrophage-like RAW 264.7 cell lines. TNF-α, IL6, and PGE2 were quantified by ELISA. The normalization of the fold change in the expression of the target gene mRNA was done by comparing the values of the ß-actin housekeeping gene using the 2-ΔΔCt comparative cycle threshold. The expression of TNF-α, IL6, iNOS, and COX-2 in the RAW 264.7 macrophage cells was analyzed using flow cytometry. Our results showed that BDK (150-350 µl/ml) treatment significantly decreased the inflammatory cytokines (TNF-α, and IL-6) and inflammatory mediators (PGE2) in LPS-stimulated RAW 264.7 macrophage cells. The pro-inflammatory cytokines (TNF-α, IL-1ß, and IL-6) expression, inflammatory enzymes (iNOS and COX-2), and NF-κBp65 were significantly downregulated at transcriptome level in LPS-stimulated RAW 264.7 macrophage cells. The flow cytometry analysis revealed that BDK treatment diminished the TNF-α, IL-6, iNOS, and COX-2 expression at the proteome level, as well as obstruction of NF-κB-p65 nuclear translocation was observed by immunofluorescence analysis in LPS-stimulated RAW 264.7 macrophage cells. Collectively, BDK can intensely augment the anti-inflammatory activities via inhibiting the NF-κB signaling pathway trigger for treating autoimmune disorders including RA.

Advances in computational frameworks in the fight against TB: The way forward.

Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

Robust anti-inflammatory activity of genistein against neutrophil elastase: a microsecond molecular dynamics simulation study.

Human Neutrophil Elastase (HNE) is one of the major causes of tissue destruction in numerous chronic and inflammatory disorders and has been reported as a therapeutic target for inflammatory diseases. Overexpression of this enzyme plays a critical role in the pathogenesis of rheumatoid arthritis (RA). The focus of this study is to identify potent natural inhibitors that could target the active site of the HNE through the use of computational methods. The molecular structure of small molecules was retrieved from several natural compound databases. This was followed by structure-based virtual screening, molecular docking, ADMET property predictions and molecular dynamic simulation studies to screen potential HNE inhibitors. In total, 1881 natural compounds were extracted and subjected to molecular docking studies, and 10 compounds were found to have good interactions, exhibiting the best docking scores. Genistein showed higher binding efficacy (-10.28 Kcal/mol) to HNE in comparison to other natural compounds. The conformational stability of the docked complex of the ELANE gene (HNE) with genistein was assessed using 1-microsecond molecular dynamic simulation (MDs), which reliably revealed the unique stereochemical alteration of the complex, indicating its conformational stability and flexibility. Alterations in the enzyme structure upon complex formation were further characterized through clustering analysis and linear interaction energy (LIE) calculation. The outcomes of this research propose novel potential candidates against target HNE.Communicated by Ramaswamy H. Sarma.