Arabidopsis thaliana is a short life cycle, small genome, and Brassicaceae family winter annual small flowering plants. It is popularly used as a model organism in genetics and plant biology research, and it is essential to understanding the molecular biology of many plant features, including light sensing and flower formation. It also plays a key role in the science of agronomy, and plant transcriptomics as well as genomics. When it comes to the development of multicellular creatures, transcriptional programs are crucial. The constantly active growth of different organ systems is supported by transcriptional programs. Arabidopsis embryos possess remarkable transcriptomes compared to other plant tissues comprising somatic embryo differentiation circumstances operating during plant embryogenesis. Here we show that the transcriptomic analysis of the genome dead box RNA helicase gene AT2G45810 of Arabidopsis thaliana Araport11 species which revealed the specific gene expression patterns of Arabidopsis tissue-specific information of developmental map, embryo, single cell, DNA damage, cellular interactions, pathway analysis, etc., through In Silico or computational approaches. In this particular study, we used the TAIR, Phytozome, and plant comparative genomics portal for retrieving and identification of specific genes of interest. Next, we used web-based Bar utoronto tools to visualize other data, including functional genomics. Their protein and gene expression tools facilitate the exploration of promoters, the identification of protein-protein interactions, the viewing of expression patterns as electronic fluorescent pictographs or heatmaps, and more.

The re-emergence of the Mpox virus (MPXV) as a global public health threat highlights the critical limitations of existing antivirals, which predominantly target viral replication while neglecting host-mediated immunopathology and severe clinical symptomatology. To address this therapeutic gap, we developed a system pharmacology-mediated trimodal framework from a comprehensive cheminformatics and medicinal chemistry workflow integrating quantum mechanics, molecular dynamics, network topology, and AI-driven retrosynthetic analysis. Here, we report the discovery of three unique functional leads selected via ligand-based virtual screening (LBVS) targeting the highly conserved viral VP39 methyltransferase (PDB: 8B07), a pivotal enzyme in viral mRNA cap formation and replication from a focused library enriched for structural analogs of validated inhibitors. Following viral target preparation and ligand library construction, candidates underwent molecular docking and ADMET profiling, which predicted favorable oral bioavailability and high safety margins. Electronic stability and chemical reactivity were validated via DFT frontierorbital analysis, while 200-ns MD simulations confirmed dynamic stability. Crucially, following simulation, thermodynamic efficacy for the top lead complexes was substantiated by MM-GBSA binding free energies (ΔG bind) ranging from –23.44 to –25.49 kcal mol–1, significantly outperforming the reference inhibitor. Pivotally, biological activity spectra and network pharmacology analysis revealed alongside direct viral VP39 blockade, these scaffolds exhibit a synergistic, host-directed effect polypharmacological division of labor, Lead 1 act as a potent viral egress inhibitor targeting host kinases (ABL1/SRC), Lead 2 functions as an immunomodulator via Toll-like receptor (TLR9) and GPCR pathways and Lead 3 offers a novel symptomatic management strategy by targeting voltage-gated sodium channels (SCN9A). Finally, the synthetic route prediction confirmed the translational feasibility of these candidates from commercially available precursors. This study establishes a rational, multi-scale computational strategy for discovering next-generation Mpox therapeutics that simultaneously arrest viral spread, potentiate host immunity, and alleviate debilitating symptoms.

Inherited factors account for a large share of breast cancer susceptibility, yet the biological consequences of most risk variants are still poorly understood. To address this gap, we studied 175 breast cancer risk variants confirmed by genome‐wide association studies and gathered the genes that lie near them. Two complementary gene sets were assembled. The first collected every gene immediately flanking each risk variant, regardless of distance. The second kept only the closest gene together with genes tagged by variants in strong linkage disequilibrium within a 100 kb interval. Across both sets, the clearest biological signal pointed to androgen receptor‐mediated signaling. Pathway mapping additionally implicated the Wnt/β‐catenin and Hedgehog cascades, and this pattern was most evident in the flanking‐gene set. With HaploReg, we annotated correlated variants at each locus and recovered features that mark active regulation. Enrichment testing returned a 4.8‐fold excess of DNase I hypersensitivity in a breast cancer cell line and a 15.7‐fold excess of enhancer motifs in a stem cell line. Together, these results indicate that breast cancer risk markers and their correlated variants influence the activity of neighboring genes and converge on a small number of signaling pathways central to mammary biology.

The resurgence of the Mpox virus (MPXV) highlights the urgent need for scalable, mutation-resistant countermeasures. Slow development timelines hinder traditional empirical vaccine discovery, while conventional vaccinology often yields high false-positive rates because it relies on static, linear sequence-based screening. These classical informatics filters consistently fail to predict physical structural stability or the dynamic presentation of immune cells under physiological conditions, creating a distinct translational gap between computational design and in vitro efficacy. To address these limitations, here we engineered an advanced, AI-augmented vaccine discovery pipeline targeting the most virulent components of the MPXV proteome. We systematically screened eight critical viral targets essential for replication, membrane morphogenesis, and host-cell entry to map highly conserved T-cell and B-cell epitopes. Moving beyond standard heuristic filtering, we implemented a high-fidelity deep learning validation layer: ESM-2 protein language models (pLMs) were deployed to calculate pseudo-perplexity scores across chimeric linker junctions (EAAAK, GPGPG) to guarantee structural naturalness, MHCflurry ensemble models successfully verified active intracellular antigen presentation, and structure-informed graph neural networks (GNNs) simulated the physical node-attention dynamics of the immune synapse. Finally, recurrent neural networks (RNNs)-driven mRNA kinetic modeling optimized translation initiation rates and minimum free energy profiles during in silico cloning. This multi-layered pipeline successfully yielded three structurally refined, multi-epitope vaccine constructs optimized for maximum global population coverage. ESM-2 structural validation confirmed exceptionally low perplexity scores at structural junctions, indicating that the engineered chimaeras have native-like stability and a minimized risk of proteolysis. GNN-based immune synapse modeling demonstrated highly stable binding kinetics and robust binding free energies between the target epitopes and human HLA alleles. Furthermore, RNN kinetic optimizations established that the cDNA sequences are optimized for high-yield expression in heterologous expression hosts without risk of ribosomal stalling or translational bottlenecks. This study establishes a deep learning-augmented framework that bridges the gap between theoretical sequence optimization and biopharmaceutical scalability. By validating the structural naturalness, real-world immunogenicity, and manufacturing kinetics of our vaccine chimeras, this framework presents a robust, proactive paradigm for rapid vaccine architecture against emerging orthopoxvirus threats.