Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli’s (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism’s metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli’s AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.

Background/Objectives: ATPase family AAA domain-containing protein 2 (ATAD2) is an oncogenic chromatin regulator that amplifies E2F/MYC transcriptional programs, yet direct modulators remain scarce. Arecoline (ARE), the primary alkaloid of the areca nut, is a known carcinogen but paradoxically exhibits context-dependent anti-proliferative activities. In this study, we resolve this paradox by defining ARE’s anti-cancer mechanism. Methods: Breast cancer cell proliferation and colony formation assays were performed to evaluate the anti-proliferative effects of ARE. Cell-cycle distribution was analyzed to determine phase-specific effects. Transcriptomic profiling was conducted to identify affected gene networks. An unbiased Cellular Thermal Shift Assay–Mass Spectrometry (CETSA-MS) screening was used to identify direct protein targets, followed by CETSA–Western blotting for validation. Finally, in silico structure-based design was applied to generate novel derivatives with improved predicted properties. Results: ARE suppressed breast cancer cell proliferation and colony formation by inducing G1/S phase arrest. Transcriptomic analysis revealed that this phenotype was driven by profound suppression of the E2F/Cell Cycle gene network. CETSA-MS identified ATAD2 through multi-omics convergence, as the 67 direct targets were collectively most significantly enriched in the E2F pathway. CETSA–Western blotting confirmed that ARE binds and thermally stabilizes ATAD2. Mechanistically, ARE engagement of ATAD2 led to downregulation of key downstream proteins, including MYC and Cyclin D1, directly linking target modulation to G1/S arrest. Structure-based design further yielded novel derivatives with predicted enhanced ATAD2 binding and substantially reduced toxicity. Conclusions: Together, these findings uncover ATAD2 as a druggable target of ARE, establish proof-of-concept for repurposing this scaffold, and provide a rational framework for developing safer ATAD2-targeted therapies.