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Updated: Jul 27, 2025
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Time-resolved Förster Resonance Energy Transfer Assays for Measurement of Endogenous Phosphorylated STAT Proteins in Human Cells
Published on: September 9, 2021
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Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning
Neha Cheemalavagu1,2,3, Karsen E Shoger2,3, Yuqi M Cao2,3
1University of Pittsburgh, Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA USA.
Biorxiv : the Preprint Server for Biology
|June 9, 2023
Summary
This study links STAT signaling dynamics to gene expression using a computational model. It predicts how cytokine signals impact macrophage gene activity, aiding future research into STAT-driven diseases.
Area of Science:
- Cellular signaling and gene regulation
- Computational biology and systems immunology
Background:
- The JAK-STAT pathway integrates cytokine signals, but STAT transcription factor function diversity remains unclear.
- Understanding STAT specificity is crucial for deciphering complex cellular responses to cytokines like IL-6 and IL-10.
Approach:
- Developed a computational workflow integrating mechanistic and machine learning models.
- Modeled macrophage responses to IL-6 and IL-10, analyzing STAT phosphorylation dynamics.
- Predicted and validated gene expression changes upon JAK2 inhibition.
Key Points:
- Identified cytokine-induced gene sets linked to late pSTAT3 dynamics.
- Observed preferential pSTAT1 reduction with JAK2 inhibition.
- Discovered dynamically regulated genes sensitive or insensitive to JAK2 variation.
Conclusions:
- Successfully linked STAT signaling dynamics to specific gene expression patterns.
- Provides a foundation for multi-level predictive models of signaling system outputs.
- Supports future efforts targeting STAT-driven gene sets in pathology.
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