Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by altered brain connectivity and function. In this study, we employed advanced bioinformatics and explainable AI to analyze gene expression associated with ASD, using data from five GEO datasets. Among 351 neurotypical controls and 358 individuals with autism, we identified 3,339 Differentially Expressed Genes (DEGs) with an adjusted p-value (≤ 0.05). A subsequent meta-analysis pinpointed 342 DEGs (adjusted p-value ≤ 0.001), including 19 upregulated and 10 down-regulated genes across all datasets. Shared genes, pathogenic single nucleotide polymorphisms (SNPs), chromosomal positions, and their impact on biological pathways were examined. We identified potential biomarkers (HOXB3, NR2F2, MAPK8IP3, PIGT, SEMA4D, and SSH1) through text mining, meriting further investigation. Additionally, we shed light on the roles of RPS4Y1 and KDM5D genes in neurogenesis and neurodevelopment. Our analysis detected 1,286 SNPs linked to ASD-related conditions, of which 14 high-risk SNPs were located on chromosomes 10 and X. We highlighted potential missense SNPs associated with FGFR inhibitors, suggesting that it may serve as a promising biomarker for responsiveness to targeted therapies. Our explainable AI model identified the MID2 gene as a potential ASD biomarker. This research unveils vital genes and potential biomarkers, providing a foundation for novel gene discovery in complex diseases.

Molecular insights of the G2019S substitution in LRRK2 kinase domain associated with Parkinson's disease: A molecular dynamics simulation approach.

The G2019S substitution in the Leucine-rich repeat kinase 2 (LRRK2) is significantly associated with Parkinson's disease (PD). This substitution was identified in both familial and sporadic forms of PD with a higher frequency. Few computational studies have reported the impact of G2019S substitution on inhibitors of the kinase domain of LRRK2. However, no computational study deeply investigated the possible impact of the G2019S substitution on the kinase domain in its Apo conformation. Therefore, in this study, we used 200 ns molecular dynamic simulation using the GROMACS 5.1.4 package software to investigate the impact of the G2019S substitution on the structure of the kinase domain of LRRK2. Our results indicate that the G2019S substitution affects the dynamics and stability of LRRK2 by decreasing the flexibility and increasing the compactness of the kinase domain and showing its tendency to be in an active conformation for long time interval because of the high energy barrier between active and inactive conformation. This study predicts the molecular pathogenicity mechanism of the G2019S on patients with PD and provides a potential platform for developing therapeutics for patients with PD that harbor this amino acid substitution.

Nanoparticle based insulin delivery system: the next generation efficient therapy for Type 1 diabetes.

Diabetic cases have increased rapidly in recent years throughout the world. Currently, for type-1 diabetes mellitus (T1DM), multiple daily insulin (MDI) injections is the most popular treatment throughout the world. At this juncture, researchers are trying to develop different insulin delivery systems, especially through oral and pulmonary route using nanocarrier based delivery system. This next generation efficient therapy for T1DM may help to improve the quality of life of diabetic patients who routinely employ insulin by the subcutaneous route. In this paper, we have depicted various next generation nanocarrier based insulin delivery systems such as chitosan-insulin nanoparticles, PLGA-insulin nanoparticles, dextran-insulin nanoparticles, polyalkylcyanoacrylated-insulin nanoparticles and solid lipid-insulin nanoparticles. Modulation of these insulin nanocarriers may lead to successful oral or pulmonary insulin nanoformulations in future clinical settings. Therefore, applications and limitations of these nanoparticles in delivering insulin to the targeted site have been thoroughly discussed.

Targeting PilA in Acinetobacter baumannii: A Computational Approach for Anti-Virulent Compound Discovery.

Acinetobacter baumannii (A. baumannii) has emerged as a critical global pathogen due to its ability to acquire resistance traits. This bacterium exhibits two distinct forms of motility: twitching, mediated by type IV pili (T4P), and surface-associated motility, independent of appendages. T4P is crucial in various bacterial species, facilitating twitching motility, biofilm formation, and host-cell adhesion. The synthesis of T4P is a common feature among Gram-negative pathogens, particularly A. baumannii, suggesting that PilA could be a viable target for biofilm-related treatments. This study aims to develop drug molecules to mitigate A. baumannii virulence by targeting PilA. Using Schrodinger software, we screened 60,766 compounds from the CMNPD, ChemDiv, and Enamine antibacterial databases through high-throughput virtual screening. The top two compounds from each database, identified through extra precision (XP) mode, were subjected to further studies. Among the six compounds identified (CMNPD18469, CMNPD20698, Z2377302405, Z2378175729, N039-0021, and N098-0051), docking scores ranged from - 5.0 to - 7.5 kcal/mol. Subsequently, we conducted 300 ns molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) analysis of the PilA-ligand complexes. Analysis of the simulation trajectories indicated structural stability and consistent behavior of the PilA-ligand complexes in a dynamic environment. Notably, the PilA-N098-0051 complex exhibited enhanced stability and robust binding interactions, underscoring its potential as a therapeutic agent. These findings suggest that the identified compounds, particularly N098-0051, hold promise as potent molecules targeting PilA, necessitating further validation through in vitro and in vivo studies.

Deciphering the miRNA-mRNA Interaction Landscape between Breast Cancer and Triple-Negative Breast Cancer: An Integrated Bioinformatics Approach.

Breast cancer (BC) is globally recognized as the second most prevalent form of cancer. It predominantly affects women and can be categorized into distinct types based on the overexpression of specific cancer receptors.The key receptors implicated in this context are the human epidermal growth factor receptor-2 (HER2), estrogen receptor (ER), and progesterone receptor (PR), alongside a particularly intricate subclass known as triple-negative breast cancer (TNBC). This subclassification is critical for the stratification of breast cancer and informs therapeutic decision-making processes. Due to a lack of therapeutic targets, such as growth factor receptors, TNBC is the most aggressive type. Hence, identifying targetable regulators such as miRNAs could pave the way for potential therapeutic interventions. To identify common differentially expressed mRNAs (DE-mRNAs) in BC, including TNBC, we leveraged two data sets from the GEO collection and The Cancer Genome Atlas (TCGA). Significant DE-mRNAs were identified through PPI, MCODE, CytoNCA, and CytoHubba analyses. Following this, miRNAs were predicted using mirDIP. We utilized GSE42568, GSE185645, and TCGA and identified 159 common DE-mRNAs. Using Cytoscape plug-ins, we identified the 10 most significant DE-mRNAs in BC. Using mirDIP, target miRNAs for 10 DE-mRNAs were identified. We conducted an advanced analysis on the TNBC GEO data set (GSE45498) to corroborate the significance of shared DE-mRNAs and DE-miRNAs in TNBC. We identified four downregulated DE-miRNAs, including hsa-miR-802, hsa-miR-1258, hsa-miR-548a-3p, and hsa-miR-2053, significantly associated with TNBC. Our study revealed significant miRNA-mRNA interactions, specifically hsa-miR-802/MELK, hsa-miR-1258/NCAPG, miR-548a-3p/CCNA2, and hsa-miR-2053/NUSAP1, in both BC and TNBC. The observed downregulation of hsa-miR-548a-3p is associated with diminished survival rates in BC patients, emphasizing their potential utility as prognostic indicators. Furthermore, the differential expression of mRNAs, including CCNB2, UBE2C, MELK, and KIF2C, correlates with reduced survival outcomes, signifying their critical role as potential targets for therapeutic intervention in both BC and TNBC. These findings highlight specific regulatory mechanisms that are potentially crucial for understanding and treating these cancer types.

Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence.

BACKGROUND: The incidence rate of oropharyngeal squamous cell carcinoma (OPSCC) worldwide is alarming. In the clinical community, there is a pressing necessity to comprehend the etiology of the OPSCC to facilitate the administration of effective treatments. METHODS: This study confers an integrative genomics approach for identifying key oncogenic drivers involved in the OPSCC pathogenesis. The dataset contains RNA-Sequencing (RNA-Seq) samples of 46 Human papillomavirus-positive head and neck squamous cell carcinoma and 25 normal Uvulopalatopharyngoplasty cases. The differential marker selection is performed between the groups with a log2FoldChange (FC) score of 2, adjusted p-value < 0.01, and screened 714 genes. The Particle Swarm Optimization (PSO) algorithm selects the candidate gene subset, reducing the size to 73. The state-of-the-art machine learning algorithms are trained with the differentially expressed genes and candidate subsets of PSO. RESULTS: The analysis of predictive models using Shapley Additive exPlanations revealed that seven genes significantly contribute to the model's performance. These include ECT2, LAMC2, and DSG2, which predominantly influence differentiating between sample groups. They were followed in importance by FAT1, PLOD2, COL1A1, and PLAU. The Random Forest and Bayes Net algorithms also achieved perfect validation scores when using PSO features. Furthermore, gene set enrichment analysis, protein-protein interactions, and disease ontology mining revealed a significant association between these genes and the target condition. As indicated by Shapley Additive exPlanations (SHAPs), the survival analysis of three key genes unveiled strong over-expression in the samples from "The Cancer Genome Atlas". CONCLUSIONS: Our findings elucidate critical oncogenic drivers in OPSCC, offering vital insights for developing targeted therapies and enhancing understanding its pathogenesis.

A computational examination of the therapeutic advantages of fourth-generation ALK inhibitors TPX-0131 and repotrectinib over third-generation lorlatinib for NSCLC with ALK F1174C/L/V mutations.

Background: In non-small-cell lung cancer (NSCLC), a pivotal factor in promoting cancer development is the rearrangement in the anaplastic lymphoma kinase ALK gene, resulting in elevated ALK protein expression. F1174C/L/V is the acquired secondary resistant mutation in ALK. Significant survival improvements have been seen while tyrosine kinase inhibitors specifically target ALK. Nevertheless, the emergence of drug resistance hinders the clinical effectiveness of these drugs. Objective: This research sought to find the binding affinity/inhibitory effects of the existing drug lorlatinib (LOR) and upcoming TPX-0131 (zotizalkib/TPX) and repotrectinib (TPX-0005/REP) inhibitors against ALK F1174C/L/V mutations using computational approaches to identify potential strategies over resistance. Methods: We conducted molecular docking, molecular dynamics simulation, and MMPBSA calculations to investigate how compact macrocyclic inhibitors, such as TPX-0131 and repotrectinib, fit within the ATP-binding boundary and differ from LOR. Results: Our results demonstrated that TPX-0131 and repotrectinib contributed to higher binding energy in F1174C and F1174L mutations than LOR. Repotrectinib showed greater binding energy in the F1174V mutation, whereas LOR and TPX-0131 exhibited similar binding energy. However, all three inhibitors showed significant binding energy toward F1174C/L/V mutations found in NSCLC. Conclusion: This comparative study of the potential binding effects of fourth-generation inhibitors TPX-0131 and repotrectinib and third-generation inhibitor LOR for ALK F1174C/L/V mutations revealed the atomistic insights of the binding mechanism. These computational findings enable us to carry out further research for the clinical implementation of fourth-generation ALK inhibitors on ALK-positive NSCLC.

Inhibition of the ATPase Domain of Human Topoisomerase IIa on HepG2 Cells by 1, 2-benzenedicarboxylic Acid, Mono (2-ethylhexyl) Ester: Molecular Docking and Dynamics Simulations.

BACKGROUND: The major attention has been received by the natural products in the prevention of diseases due to their pharmacological role. OBJECTIVE: The major focus of the study was to search for highly potential anti-cancer compounds from marine Streptomyces sp. VITJS4 (NCIM No. 5574). METHODS: Cytotoxic assay was examined by MTT assay on HepG2 cells. Bioassay-guided fractionation of the ethyl acetate extract from the fermented broth led to the isolation of the compound. The lead compound structure was elucidated by combined NMR and MS analysis, and the absolute configuration was assigned by extensive spectroscopic analysis. RESULTS: On the basis of spectroscopic data, the compound was identified as 1, 2 benzenedicarboxylic acid, mono 2-ethylhexyl (BMEH). The compound exhibited in vitro anticancer potential against liver (HepG2) cancer cells. Based on the flow cytometric analysis, it was evident that the BMEH was also effective in arresting the cell cycle at G1 phase. Further, the Western blotting analysis confirmed the down-regulation of Bcl-2 family proteins, and activation of caspase-9 and 3. The molecular docking and dynamics simulation were performed to reveal the activity of the compound over a time period of 10ns. From the molecular dynamics studies, it was found that the stability and compactness were attained by the protein by means of the compound interaction. CONCLUSION: This study highlights our collaborative efforts to ascertain lead molecules from marine actinomycete. This is the first and foremost report to prove the mechanistic studies of the purified compound 1, 2-benzene dicarboxylic acid, mono(2-ethylhexyl) ester isolated from marine Streptomyces sp.VITJS4 against HepG2 cells.

TNF/TNFR: drug target for autoimmune diseases and immune-mediated inflammatory diseases.

Tumor necrosis factor, a regulatory cytokine, is extremely important signaling protein in the immune system. Among TNF family, TNF-alpha, TNF-beta are most the significant family members. Receptor of TNF namely TNFR1 and TNFR2 stimulates two different signaling pathways. TNFR1 signaling induces apoptosis pathway. Conversely, TNFR2 signaling triggers cell survival pathways. In this paper, we discuss about the TNF family with special reference to TNF-alpha/TNF-beta, different hypothesis related to autoimmunity and role of TNF, structure of TNF-alpha/TNF-beta, distribution and normal activity in human body of TNF, receptors and signaling pathway for drug targeting. Finally, we also discuss about the therapy for autoimmune diseases and immune-mediated inflammatory diseases (IMIDs) using small molecules or therapeutic proteins.

Structural signature of the G719S-T790M double mutation in the EGFR kinase domain and its response to inhibitors.

Some individuals with non-small-cell lung cancer (NSCLC) benefit from therapies targeting epidermal growth factor receptor (EGFR), and the characterization of a new mechanism of resistance to the EGFR-specific antibody gefitinib will provide valuable insight into how therapeutic strategies might be designed to overcome this particular resistance mechanism. The G719S and T790M mutations and their combination were involved in causing different conformational redistribution of EGFR. In the present computational study, we analyzed the impact and structural influence of G719S/T790M double mutation (DM) in EGFR with ligand (gefitinib) through molecular dynamic simulation (50 ns) and docking analysis. We observed the escalation in distance between the functional loop and activation loop with respect to T790M mutation compared to the G719S mutation. Furthermore, we confirmed that the G719S mutation causes the ligand to move closer to the hinge region, whereas T790M makes the ligand escape from the binding pocket. Obtained results provide with an explanation for the resistance induced by T790M and a vital clue for the design of drugs to combat gefitinib resistance.

Nanoparticles as 'smart' pharmaceutical delivery.

Pharmaceuticals in conjunction with nanoparticle delivery systems are growing towards new heights. The aim of this review is to gain a thorough understanding of different types and characteristics of nanoparticle based delivery systems, important properties of delivery systems, pharmaceutical ingredient loading and release in the nanoparticle delivery systems. In this review, we have also highlighted about the promising pharmaceutical deliveries like brain targeted delivery, ocular delivery, oral delivery, dermal and transdermal delivery, cancer chemotherapy, vaccine delivery, nucleic acids delivery and delivery system coupling to implants. A snapshot of the nanoparticle mediated drug deliveries which are commercially available and ongoing clinical trials have been provided.