This study demonstrates that ertapenem exhibits potent antibacterial activity against S. aureus and is a potential inhibitor of biofilm formation, and implicates a novel aspect of ertapenem’s activity connected to the disruption of purine biosynthesis in S. aureus through targeted downregulation of FGAM synthase.
Abstract
Staphylococcus aureus is a major cause of both nosocomial and community-acquired infections, imposing a significant burden on healthcare systems owing to its capacity for mature biofilm formation. These biofilms create polymer-based matrices that reduce bacterial vulnerability to immune responses and antimicrobial agents, complicating the treatment of drug-resistant S. aureus strains and underscoring the need for novel antibacterial targets. Here, we provide the first evidence that FGAM synthase (PurQ), an essential enzyme in the de novo purine biosynthesis pathway, is a putative intracellular protein target associated with ertapenem exposure in S. aureus. Our study demonstrates that ertapenem exhibits potent antibacterial activity against S. aureus (MIC = 1 μg/mL) and is a potential inhibitor of biofilm formation. Mechanistically, this antibiofilm activity correlates with selective downregulation of purine biosynthesis proteins. Using LC-MS/MS analysis of biofilm-derived adherent S. aureus cells, we quantified 1,706 proteins and identified 59 proteins with statistically significant expression changes in ertapenem-treated versus untreated S. aureus cells (49 downregulated, 10 upregulated). Proteins associated with purine biosynthesis were predominantly downregulated, particularly FGAM synthase (PurQ) with a −2.29 log2 fold change. Molecular docking supported by 100 ns molecular dynamics simulations demonstrated stable ertapenem binding to the PurQ active site (docking score: −9.615 kcal/mol) through interactions with key residues: Gly53, Asp54, Tyr55, Gln89, His141, Gly142, Glu143, and Gly144. Collectively, these findings implicate a novel aspect of ertapenem’s activity, connected to the disruption of purine biosynthesis in S. aureus through targeted downregulation of FGAM synthase. This work provides a strong foundation for drug repurposing strategies aimed at targeting purine biosynthesis in drug-resistant bacterial pathogens.
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.