黄色ブドウ球菌を迅速識別する色変化検査の開発（Colour test ‘sniffs out’ dangerous staph strains fast）

2026-04-16 ロイヤルメルボルン工科大学（RMIT）

＜関連情報＞

• https://www.rmit.edu.au/news/all-news/2026/apr/staph-rapid-test
• https://onlinelibrary.wiley.com/doi/10.1002/smll.202512266

毒性が強く抗生物質耐性を持つ黄色ブドウ球菌株を予測的に検出するためのナノザイムアプタセンサーアレイ Nanozyme Aptasensor Array for Predictive Sensing of Virulent and Antibiotic-Resistant Staphylococcus Aureus strains

Pabudi Weerathunge, Mahdieh Yazdani, Tarun K. Sharma, Wilson K. M. Wong, Mugdha V. Joglekar, Anandwardhan A. Hardikar, Vincent M. Rotello, Rajesh Ramanathan, Vipul Bansal
Small  Published: 11 February 2026
DOI:https://doi.org/10.1002/smll.202512266

ABSTRACT

Staphylococcus aureus, an important human pathogen, is the leading cause of infection-related death globally. It stands out as the only bacterial pathogen, apart from Mycobacterium tuberculosis, responsible for over a million fatalities each year. The emergence of antibiotic-resistant strains, such as methicillin-resistant S. aureus (MRSA), has created challenges in detecting S. aureus infections, as treatment depends on identifying the specific strain causing the infection. This study highlights the use of an array-based colorimetric aptasensor platform using aptamers, which exhibit specific binding across different S. aureus strains. This platform generates unique colorimetric fingerprints for different S. aureus strains, thus enabling an unbiased and strain-specific detection system. The unique signatures arise from differences in the dissociation dynamics of aptamers on the surface of nanozymes. The sensor response was analysed using pattern recognition tools trained on responses from the aptasensor array to identify individual S. aureus strains. Furthermore, the sensing platform offers additional functionality by providing information about the virulence factors associated with pathogenicity, such as the presence of markers like Panton-Valentine leukocidin (pvl), which is a marker of increased virulence and sensitivity/resistance to antibiotics. The platform would be capable of recognising previously unencountered S. aureus strains, enabling predictive capabilities and utility in clinical diagnostics.