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已发表论文
通过宏基因组下一代测序技术探索中枢神经系统感染中的微生物群谱系
Authors Wang JM, Pan YY, Hong JC, Jiang ZJ, Zhang SY, Fan RJ, Yang BH, Wang ZQ, Zhu JT, Yao XP
Received 8 July 2025
Accepted for publication 24 October 2025
Published 17 November 2025 Volume 2025:18 Pages 6019—6033
DOI https://doi.org/10.2147/IDR.S552138
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Yan Li
Jun-Mei Wang,1,2,* Yu-Ying Pan,1,2,* Jian-Chen Hong,3,* Zai-Jie Jiang,1 Shi-Ying Zhang,1 Rui-Jie Fan,1 Bi-Hui Yang,1 Zhi-Qiang Wang,1 Ji-Ting Zhu,1,2 Xiang-Ping Yao1,2
1Department of Neurology, Fujian Institute of Neurology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China; 2Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, People’s Republic of China; 3Department of Gastrointestinal Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiang-Ping Yao, Department of Neurology, Fujian Institute of Neurology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China, Email yaoxiangping@fjmu.edu.cn
Purpose: This study leveraged CSF metagenomic next-generation sequencing (mNGS) to bridge this knowledge gap and elucidate the microbiota spectrum of CNS infections.
Patients and Methods: We retrospectively analyzed CSF mNGS reports and clinical data from 264 patients with suspected CNS infections, who were enrolled from September 2019 to November 2023.
Results: According to diagnostic criteria, 145 patients were diagnosed with CNS infections, including bacterial (27 cases, 18.6%), Mycobacterium tuberculosis (30, 20.7%), fungal (23, 15.9%), and viral (65, 44.8%) infections. The mNGS positive detection rate was 46.2% (67/145), with significant differences among groups (p < 0.001). A total of 22 pathogens were identified, most commonly Cryptococcus neoformans (16, 23.9%), Mycobacterium tuberculosis (10, 14.9%), and Epstein-Barr virus (9, 13.4%). The most frequent background microorganisms detected by mNGS were Cutibacterium acnes (58.6%), Moraxella osloensis (29.0%), and Malassezia restricta (26.2%).
Conclusion: High-throughput sequencing using mNGS revealed the microbial compositions in CSF samples from patients with CNS infections. This approach may enhance our understanding of pathogens and assist clinicians in making effective therapeutic decisions.
Keywords: central nervous system infection, microbiota, metagenomic next-generation sequencing, cerebrospinal fluid
1Department of Neurology, Fujian Institute of Neurology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China; 2Department of Neurology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, People’s Republic of China; 3Department of Gastrointestinal Surgery, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xiang-Ping Yao, Department of Neurology, Fujian Institute of Neurology, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People’s Republic of China, Email yaoxiangping@fjmu.edu.cn
Purpose: This study leveraged CSF metagenomic next-generation sequencing (mNGS) to bridge this knowledge gap and elucidate the microbiota spectrum of CNS infections.
Patients and Methods: We retrospectively analyzed CSF mNGS reports and clinical data from 264 patients with suspected CNS infections, who were enrolled from September 2019 to November 2023.
Results: According to diagnostic criteria, 145 patients were diagnosed with CNS infections, including bacterial (27 cases, 18.6%), Mycobacterium tuberculosis (30, 20.7%), fungal (23, 15.9%), and viral (65, 44.8%) infections. The mNGS positive detection rate was 46.2% (67/145), with significant differences among groups (p < 0.001). A total of 22 pathogens were identified, most commonly Cryptococcus neoformans (16, 23.9%), Mycobacterium tuberculosis (10, 14.9%), and Epstein-Barr virus (9, 13.4%). The most frequent background microorganisms detected by mNGS were Cutibacterium acnes (58.6%), Moraxella osloensis (29.0%), and Malassezia restricta (26.2%).
Conclusion: High-throughput sequencing using mNGS revealed the microbial compositions in CSF samples from patients with CNS infections. This approach may enhance our understanding of pathogens and assist clinicians in making effective therapeutic decisions.
Keywords: central nervous system infection, microbiota, metagenomic next-generation sequencing, cerebrospinal fluid