Modeling the complete kinetics of coxsackievirus B3 reveals human determinants of host-cell feedback Graphical abstract Highlights d A complete kinetic model of acute infection by the coxsackievirus B3 enterovirus d Enteroviral replication organelles accelerate biochemistry on membrane surfaces d Type I interferon exaggerates different enteroviral susceptibilities of host cells d A common polymorphism in MAVS alters host-cell susceptibility to coxsackievirus B3
, and the nucleus. The extracts contain all major classes of protein phosphatases and catalyze dephosphorylation of plate-bound phosphosubstrates in a microtiter format, with cellular activity quantified at the end point by phosphospecific ELISA. The platform is optimized for six phosphosubstrates (ERK2, JNK1, p38␣, MK2, CREB, and STAT1) and measures specific activities from extracts of fewer than 50,000 cells. The assay was exploited to examine viral and antiviral signaling in AC16 cardiomyocytes, which we show can be engineered to serve as susceptible and permissive hosts for CVB3. Phosphatase responses were profiled in these cells by completing a full-factorial experiment for CVB3 infection and type I/II interferon signaling. Over 850 functional measurements revealed several independent, subcellular changes in specific phosphatase activities. During CVB3 infection, we found that type I interferon signaling increases subcellular JNK1 phosphatase activity, inhibiting nuclear JNK1 activity that otherwise promotes viral protein synthesis in the infected host cell. Our assay provides a high-throughput way to capture perturbations in important negative regulators of intracellular signaltransduction networks. Molecular & Cellular Proteomics 16:
Cells respond to DNA damage by activating complex signaling networks that decide cell fate, promoting not only DNA damage repair and survival but also cell death. We have developed a multiscale computational model that quantitatively links chemotherapy-induced DNA damage response signaling to cell fate. The computational model was trained and calibrated on extensive data from U2OS osteosarcoma cells, including the cell cycle distribution of the initial cell population, signaling data measured by Western blotting, and cell fate data in response to chemotherapy treatment measured by time-lapse microscopy. The resulting mechanistic model predicted the cellular responses to chemotherapy alone and in combination with targeted inhibitors of the DNA damage response pathway, which we confirmed experimentally. Computational models such as the one presented here can be used to understand the molecular basis that defines the complex interplay between cell survival and cell death and to rationally identify chemotherapy-potentiating drug combinations.
Aims
To develop a quantitative systems pharmacology model to describe the effect of dapagliflozin (a sodium‐glucose co‐transporter‐2 [SGLT2] inhibitor) on glucose‐insulin dynamics in type 2 diabetes mellitus (T2DM) patients, and to identify key determinants of treatment‐mediated glycated haemoglobin (HbA1c) reduction.
Materials and methods
Glycaemic control during dapagliflozin treatment was mechanistically characterized by integrating components representing dapagliflozin pharmacokinetics (PK), glucose‐insulin homeostasis, renal glucose reabsorption, and HbA1c formation. The model was developed using PK variables, glucose, plasma insulin, and urinary glucose excretion (UGE) from a phase IIa dapagliflozin trial in patients with T2DM (NCT00162305). The model was used to predict dapagliflozin‐induced HbA1c reduction; model predictions were compared to actual data from phase III trials (NCT00528879, NCT00683878, NCT00680745 and NCT00673231).
Results
The integrated glucose‐insulin‐dapagliflozin model successfully described plasma glucose and insulin levels, as well as UGE in response to oral glucose tolerance tests and meal intake. HbA1c reduction was also well predicted. The results show that dapagliflozin‐mediated glycaemic control is anticorrelated to steady‐state insulin concentration and insulin sensitivity.
Conclusions
The developed model framework is the first to integrate SGLT2 inhibitor mechanism of action with both short‐term glucose‐insulin dynamics and long‐term glucose control (HbA1c). The results suggest that dapagliflozin treatment is beneficial in patients with inadequate glycaemic control from insulin alone and this benefit increases as insulin control diminishes.
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