Cell cycle aberrations occurring at the G1/S checkpoint often lead to uncontrolled cell proliferation and tumor growth. We recently demonstrated that IL-1β inhibits insulin-like growth factor (IGF)-I-induced cell proliferation by preventing cells from entering the S phase of the cell cycle, leading to G0/G1 arrest. Notably, IL-1β suppresses the ability of the IGF-I receptor tyrosine kinase to phosphorylate its major docking protein, insulin receptor substrate-1, in MCF-7 breast carcinoma cells. In this study, we extend this juxtamembrane cross-talk between cytokine and growth factor receptors to downstream cell cycle machinery. IL-1β reduces the ability of IGF-I to activate Cdk2 and to induce E2F-1, cyclin A, and cyclin A-dependent phosphorylation of a retinoblastoma tumor suppressor substrate. Long-term activation of the phosphatidylinositol 3-kinase/Akt signaling pathway, but not the mammalian target of rapamycin or mitogen-activated protein kinase pathways, is required for IGF-I to hyperphosphorylate retinoblastoma and to cause accumulation of E2F-1 and cyclin A. In the absence of IGF-I to induce Akt activation and cell cycle progression, IL-1β has no effect. IL-1β induces p21Cip1/Waf1, which may contribute to its inhibition of IGF-I-activated Cdk2. Collectively, these data establish a novel mechanism by which prolonged Akt phosphorylation serves as a convergent target for both IGF-I and IL-1β; stimulation by growth factors such as IGF-I promotes G1-S phase progression, whereas IL-1β antagonizes IGF-I-induced Akt phosphorylation to induce cytostasis. In this manner, Akt serves as a critical bridge that links proximal receptor signaling events to more distal cell cycle machinery.
High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, timetested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modern languages have come to expect.We present an improved binding-generation strategy based on static analysis of unannotated library source code. We characterize three high-level idioms that are not uniquely expressible in C's lowlevel type system: array parameters, resource managers, and multiple return values. We describe a suite of interprocedural analyses that recover this high-level information, and we show how the results can be used in a binding generator for the Python programming language. In experiments with four large C libraries, we find that our approach avoids the mistakes characteristic of hand-written bindings while offering a level of Python integration unmatched by prior automated approaches. Among the thousands of functions in the public interfaces of these libraries, roughly 40% exhibit the behaviors detected by our static analyses.
High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, timetested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modern languages have come to expect.We present an improved binding-generation strategy based on static analysis of unannotated library source code. We characterize three high-level idioms that are not uniquely expressible in C's lowlevel type system: array parameters, resource managers, and multiple return values. We describe a suite of interprocedural analyses that recover this high-level information, and we show how the results can be used in a binding generator for the Python programming language. In experiments with four large C libraries, we find that our approach avoids the mistakes characteristic of hand-written bindings while offering a level of Python integration unmatched by prior automated approaches. Among the thousands of functions in the public interfaces of these libraries, roughly 40% exhibit the behaviors detected by our static analyses.
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