Johnson Zachary, Anderson David, Cheung Margaret S, Bohutskyi Pavlo
Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, United States.
Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States.
Front Microbiol. 2025 Mar 26;16:1569559. doi: 10.3389/fmicb.2025.1569559. eCollection 2025.
PCC 7942 is a model organism for studying circadian regulation and bioproduction, where precise temporal control of metabolism significantly impacts photosynthetic efficiency and CO-to-bioproduct conversion. Despite extensive research on core clock components, our understanding of the broader regulatory network orchestrating genome-wide metabolic transitions remains incomplete. We address this gap by applying machine learning tools and network analysis to investigate the transcriptional architecture governing circadian-controlled gene expression. While our approach showed moderate accuracy in predicting individual transcription factor-gene interactions - a common challenge with real expression data - network-level topological analysis successfully revealed the organizational principles of circadian regulation. Our analysis identified distinct regulatory modules coordinating day-night metabolic transitions, with photosynthesis and carbon/nitrogen metabolism controlled by day-phase regulators, while nighttime modules orchestrate glycogen mobilization and redox metabolism. Through network centrality analysis, we identified potentially significant but previously understudied transcriptional regulators: HimA as a putative DNA architecture regulator, and TetR and SrrB as potential coordinators of nighttime metabolism, working alongside established global regulators RpaA and RpaB. This work demonstrates how network-level analysis can extract biologically meaningful insights despite limitations in predicting direct regulatory interactions. The regulatory principles uncovered here advance our understanding of how cyanobacteria coordinate complex metabolic transitions and may inform metabolic engineering strategies for enhanced photosynthetic bioproduction from CO.
集胞藻7942是用于研究昼夜节律调控和生物生产的模式生物，其中代谢的精确时间控制对光合效率和一氧化碳到生物产品的转化有显著影响。尽管对核心生物钟组件进行了广泛研究，但我们对协调全基因组代谢转变的更广泛调控网络的理解仍不完整。我们通过应用机器学习工具和网络分析来研究昼夜节律控制基因表达的转录结构，以弥补这一差距。虽然我们的方法在预测单个转录因子-基因相互作用方面显示出中等准确性——这是真实表达数据常见的挑战——但网络水平的拓扑分析成功揭示了昼夜节律调控的组织原则。我们的分析确定了协调昼夜代谢转变的不同调控模块，光合作用以及碳/氮代谢由白天阶段的调节因子控制，而夜间模块则协调糖原动员和氧化还原代谢。通过网络中心性分析，我们确定了潜在重要但以前研究不足的转录调节因子：HimA作为假定的DNA结构调节因子，TetR和SrrB作为夜间代谢的潜在协调因子，与已确定的全局调节因子RpaA和RpaB协同工作。这项工作展示了尽管在预测直接调控相互作用方面存在局限性，但网络水平分析如何能够提取有生物学意义的见解。这里揭示的调控原则推进了我们对蓝细菌如何协调复杂代谢转变的理解，并可能为从一氧化碳增强光合生物生产的代谢工程策略提供信息。
