Applying prior knowledge of regulatory signaling to investigate macrophage cAMP dynamics during Mycobacterium tuberculosis infection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".