Liu Wenjia, Rao Xin
School of Electronic Information Engineering, Suzhou Polytechnic University, Suzhou, 215104, China.
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, China.
Arch Microbiol. 2025 Aug 29;207(10):243. doi: 10.1007/s00203-025-04444-7.
Streptococcus pneumoniae (S. pneumoniae) is a gram-positive bacterium, which is a human pathogen that colonises the human nasopharyngeal region. The evolution of its resistance to many antibiotics has become a major clinical and public health problem. In a study of S. pneumoniae, it was found that resistant strains contained more non-synonymous single nucleotide polymorphisms (NS-SNPs) than sensitive strains. These findings motivate us to further understand the role of NS-SNP mutation in bacterial drug-resistance and how it mediates the development of S. pneumoniae drug-resistance. NS-SNP is a molecular genetic marker that has been widely used in the field of disease and microbial drug-resistance. However, few studies have analyzed the characteristics and related mechanisms of microbial drug-resistance through the effect of NS-SNP mutation on protein conformation and function. Therefore, based on NS-SNP mutation, this study predicted the homologous resistance proteins related to S. pneumoniae and explored the resistance mechanism of homologous proteins mediated by NS-SNP mutation. SNP identification was first implemented by using MUMmer 3 software for whole-genome sequence alignment. The self-designed Fast Feature Selection (FFS) and Codon Mutation Detection (CMD) machine learning algorithms were used for feature extraction and NS-SNPs detection, respectively, ten NS-SNPs mutations were finally selected. The protein/homologous protein structure was predicted and evaluated by ab initio method and Swiss-Model server. Subsequently, Molecular Operating Environment (MOE) software was used to compare protein structure and superposition. Finally, the impact of NS-SNPs on the electrostatic surface of proteins was also evaluated by PyMOL software. This study found that three NS-SNPs mutation-mediated homologous proteins were closely related to drug-resistance of S. pneumoniae, namely NS-SNPs (ID 247805, 817989) mutations-mediated antibiotic resistant ABCF (ARE-ABCF) transporter, and NS-SNP (ID 1101585) mutation-mediated NorM protein promoting antibiotic resistance of S. pneumoniae. Moreover, the resistance might be caused by the difference in electrostatic potential energy resulting from the NS-SNP mutations. This suggests that changes in the electrostatic environment might affect antibiotic binding affinity, revealing a novel mechanism of bacterial drug resistance. Furthermore, this study also provides information on the antibiotic resistance of S. pneumoniae, laying the foundation for its clinical research, diagnosis and medication to treat bacterial infections.
肺炎链球菌是一种革兰氏阳性细菌，是一种定植于人类鼻咽部的人类病原体。其对多种抗生素耐药性的演变已成为一个主要的临床和公共卫生问题。在一项对肺炎链球菌的研究中，发现耐药菌株比敏感菌株含有更多的非同义单核苷酸多态性（NS-SNPs）。这些发现促使我们进一步了解NS-SNP突变在细菌耐药性中的作用以及它如何介导肺炎链球菌耐药性的发展。NS-SNP是一种分子遗传标记，已在疾病和微生物耐药性领域广泛应用。然而，很少有研究通过NS-SNP突变对蛋白质构象和功能的影响来分析微生物耐药性的特征和相关机制。因此，本研究基于NS-SNP突变，预测了与肺炎链球菌相关的同源耐药蛋白，并探讨了NS-SNP突变介导的同源蛋白的耐药机制。首先使用MUMmer 3软件进行全基因组序列比对来实现SNP鉴定。自行设计的快速特征选择（FFS）和密码子突变检测（CMD）机器学习算法分别用于特征提取和NS-SNPs检测，最终选择了10个NS-SNPs突变。通过从头算方法和瑞士模型服务器对蛋白质/同源蛋白结构进行预测和评估。随后，使用分子操作环境（MOE）软件比较蛋白质结构并进行叠加。最后，还通过PyMOL软件评估了NS-SNPs对蛋白质静电表面的影响。本研究发现，三个NS-SNPs突变介导的同源蛋白与肺炎链球菌的耐药性密切相关，即NS-SNPs（ID 247805、817989）突变介导的抗生素抗性ABCF（ARE-ABCF）转运蛋白，以及NS-SNP（ID 1101585）突变介导的促进肺炎链球菌抗生素抗性的NorM蛋白。此外，耐药性可能是由NS-SNP突变导致的静电势能差异引起的。这表明静电环境的变化可能影响抗生素结合亲和力，揭示了一种新的细菌耐药机制。此外，本研究还提供了肺炎链球菌抗生素耐药性的信息，为其临床研究、诊断和治疗细菌感染的用药奠定了基础。
