Ma Dumei, Wang Yongqi, Ye Jiacheng, Xin Chao, Ding Chuan-Fan, Yan Yinghua
Institute of Mass Spectrometry, School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 315211, China.
The First Affiliated Hospital of Ningbo University, Ningbo 315211, China.
Anal Chem. 2025 May 13;97(18):9911-9919. doi: 10.1021/acs.analchem.5c00509. Epub 2025 May 2.
Carbapenem resistance and hypervirulence represent two distinct evolutionary pathways in , posing significant challenges in clinical settings. Of particular concern are convergent strains that combine both traits, complicating timely diagnosis and treatment. Herein, we present a novel MOF-derived bimetallic nanocube hybrid nanosheet (denoted Pt-G@CuZnC@Au) designed to enhance laser desorption/ionization mass spectrometry (LDI-MS) in distinguishing convergent strains from other variants. The novel material, synthesized through the pyrolysis of pristine MOFs, features uniformly distributed Cu and Zn synergistic metal sites within the carbon matrix, addressing critical limitations of current nanomatrices for in situ extraction of metabolic fingerprints from microbial cells, such as limited sensitivity (e.g., amorphous silicon, TiO, and metal nanoparticles) or relatively weak conductivity and stability (MOF-based materials). Utilizing this advanced matrix, the metabolic fingerprints of 248 isolates were rapidly extracted, identifying 23 top VIP-score peaks as potential biomarkers for differentiating convergent strains from their variants. Combined with machine learning, the prediction model achieved 100% accuracy in distinguishing convergent strains from carbapenem-sensitive isolates (CS_cKP) or hypervirulent isolates (hvKP) using the SVM model, while achieving 78.26% accuracy in differentiating them from carbapenem-resistant isolates (CR_cKP) with the KNN/NB models. These findings highlight the high accuracy and efficacy of our assay in distinguishing convergent strains from their variants.
碳青霉烯耐药性和高毒力代表了肺炎克雷伯菌的两种不同进化途径，在临床环境中构成了重大挑战。特别令人担忧的是兼具这两种特征的趋同菌株，这使得及时诊断和治疗变得复杂。在此，我们展示了一种新型的金属有机框架衍生双金属纳米立方杂化纳米片（表示为Pt-G@CuZnC@Au），旨在增强激光解吸/电离质谱（LDI-MS）以区分趋同菌株与其他变体。这种新型材料通过原始金属有机框架的热解合成，在碳基质中具有均匀分布的铜和锌协同金属位点，解决了当前用于从微生物细胞原位提取代谢指纹的纳米基质的关键局限性，例如灵敏度有限（如非晶硅、二氧化钛和金属纳米颗粒）或导电性和稳定性相对较弱（基于金属有机框架的材料）。利用这种先进的基质，快速提取了248株分离株的代谢指纹，确定了23个VIP得分最高的峰作为区分趋同菌株与其变体的潜在生物标志物。结合机器学习，预测模型在使用支持向量机（SVM）模型区分趋同菌株与碳青霉烯敏感分离株（CS_cKP）或高毒力分离株（hvKP）时准确率达到100%，而在使用K近邻/朴素贝叶斯（KNN/NB）模型区分趋同菌株与碳青霉烯耐药分离株（CR_cKP）时准确率达到78.26%。这些发现突出了我们的检测方法在区分趋同菌株与其变体方面的高精度和有效性。
