In this proposed model, anagrelide remodels the interactome of the PDE3A-SLFN12 complex, which relocalizes and accumulates near the mRNA entry channel of the 43S pre-initiation complex, which provides important mechanistic context for how PDE3A modulators act within the cell.
Abstract
Phosphodiesterase 3A (PDE3A) modulators such as anagrelide induce complex formation between PDE3A and Schlafen 12 (SLFN12), selectively killing cancer cells that co-express both proteins. As PDE3A forms isoform- and cell type-specific signalosome complexes across multiple subcellular compartments, the effects of its modulation are expected to depend strongly on the surrounding protein interaction network. However, despite considerable pre-clinical and early clinical interest in PDE3A modulators, the cellular context in which these compounds act has remained poorly characterized. Using proximity-dependent biotinylation in two human cancer cell lines (SA-4, liposarcoma; HeLa, cervical adenocarcinoma), we mapped the interactomes of PDE3A, SLFN12, and the anagrelide-induced PDE3A-SLFN12 complex. Anagrelide induced 259 high-confidence interactions, most notably with ribosomal proteins and translation initiation factors, while suppressing 877 interactions, most prominently those associated with the proteasome, protein folding, and the ER membrane. Interactions were selectively induced with peripheral eukaryotic translation initiation factor 3 (eIF3) subunits eIF3A and eIF3B, while interactions with core eIF3 subunits eIF3H, eIF3L, and eIF3M were suppressed. These findings were validated by native co-immunoprecipitation across three cancer cell lines, and multiplex immunofluorescence confirmed accumulation and ribosomal redistribution of both PDE3A and SLFN12 following anagrelide treatment. AlphaFold modeling of the PDE3A-SLFN12 complex with a partial 43S pre-initiation complex predicted binding near eIF3B and the eIF2αβγ-tRNA ternary complex at the mRNA entry channel. By mapping the interactomes surrounding the drug-induced complex, our study provides important mechanistic context for how PDE3A modulators act within the cell. In our proposed model, anagrelide remodels the interactome of the PDE3A-SLFN12 complex, which relocalizes and accumulates near the mRNA entry channel of the 43S pre-initiation complex.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.