Applying prior knowledge of regulatory signaling to investigate macrophage cAMP dynamics during Mycobacterium tuberculosis infection.

Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis, resides in host lung macrophages and has evolved unique processes to hijack host signaling pathways to facilitate its survival and propagation within macrophages. Notably, Mtb exports cyclic AMP (cAMP), a key regulatory signaling molecule, during infection. As can often be the case, experimental data exploring immune modulation by cAMP during Mtb infection are sparse, largely cross-sectional and offer only very partial coverage. Data-poor conditions such as this significantly challenge conventional data-driven analyses. Accordingly, we apply a hypothesis driven approach to construct a mechanistically informed network model from prior knowledge of pathway signaling recovered from manually curated pathway schema and extracted from literature. Undocumented pathway elements are hypothesized under strict confidence measures using generative artificial intelligence to ensure a closed loop architecture consistent with homeostatic stability. Simulated perturbations using the most plausible network models highlight the impact of IL-6 on cAMP response. Subsequent experimental validation using human THP-1 monocytes differentiated to macrophages supported this effect. These results suggest that the de novo creation of mechanistically informed network models from prior knowledge may support early explorations of complex pathway dynamics, such as intracellular cAMP signaling during Mtb infection, when experimental data is sparse or unavailable.