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Creators/Authors contains: "Li, Sheng"

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  1. Free, publicly-accessible full text available September 1, 2024
  2. Free, publicly-accessible full text available August 1, 2024
  3. Free, publicly-accessible full text available February 1, 2024
  4. Hyperactive sphingosine 1-phosphate (S1P) signaling is associated with a poor prognosis of triple-negative breast cancer (TNBC). Despite recent evidence that links the S1P receptor 1 (S1P1) to TNBC cell survival, its role in TNBC invasion and the underlying mechanisms remain elusive. Combining analyses of human TNBC cells with zebrafish xenografts, we found that phosphorylation of S1P receptor 1 (S1P1) at threonine 236 (T236) is critical for TNBC dissemination. Compared to luminal breast cancer cells, TNBC cells exhibit a significant increase of phospho-S1P1 T236 but not the total S1P1 levels. Misexpression of phosphorylation-defective S1P1 T236A (alanine) decreases TNBC cell migration in vitro and disease invasion in zebrafish xenografts. Pharmacologic disruption of S1P1 T236 phosphorylation, using either a pan-AKT inhibitor (MK2206) or an S1P1 functional antagonist (FTY720, an FDA-approved drug for treating multiple sclerosis), suppresses TNBC cell migration in vitro and tumor invasion in vivo. Finally, we show that human TNBC cells with AKT activation and elevated phospho-S1P1 T236 are sensitive to FTY720-induced cytotoxic effects. These findings indicate that the AKT-enhanced phosphorylation of S1P1 T236 mediates much of the TNBC invasiveness, providing a potential biomarker to select TNBC patients for the clinical application of FTY720. 
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    Free, publicly-accessible full text available April 1, 2024
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  6. Abstract A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription factor regulatory networks using both transcriptomics data and literature-based transcription factor-target databases. NetAct robustly infers regulators’ activity using target expression, constructs networks based on transcriptional activity, and integrates mathematical modeling for validation. Our in silico benchmark test shows that NetAct outperforms existing algorithms in inferring transcriptional activity and gene networks. We illustrate the application of NetAct to model networks driving TGF-β-induced epithelial-mesenchymal transition and macrophage polarization. 
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  7. Khudyakov, Yury E (Ed.)
    We construct an agent-based SEIR model to simulate COVID-19 spread at a 16000-student mostly non-residential urban university during the Fall 2021 Semester. We find that mRNA vaccine coverage at 100% combined with weekly screening testing of 25% of the campus population make it possible to safely reopen to in-person instruction. Our simulations exhibit a right-skew for total infections over the semester that becomes more pronounced with less vaccine coverage, less vaccine effectiveness and no additional preventative measures. This suggests that high levels of infection are not exceedingly rare with campus social connections the main transmission route. Finally, we find that if vaccine coverage is 100% and vaccine effectiveness is above 80%, then a safe reopening is possible even without facemask use. This models possible future scenarios with high coverage of additional “booster” doses of COVID-19 vaccines. 
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