Enhancing upper respiratory tract infection detection: exploring qPCR negative respiratory samples using targeted next-generation sequencing

Zhixia Gu; Tingting Liu; Jun Li; Chuan Song; Xinlong Wang; Ying Tang; Mo Du; Yuhai Bi; Yuanyuan Zhang; Ronghua Jin; Rui Song

doi:10.1007/s15010-025-02578-w

Enhancing upper respiratory tract infection detection: exploring qPCR negative respiratory samples using targeted next-generation sequencing

Infection. 2025 Jul 1. doi: 10.1007/s15010-025-02578-w. Online ahead of print.

Authors

Zhixia Gu^#^{1

2

3

4}, Tingting Liu^#^{1

2

3

4}, Jun Li^#⁵, Chuan Song^{1

2

3

4}, Xinlong Wang^{1

2

3

4}, Ying Tang^{1

2

3

4}, Mo Du^{1

2

3

4}, Yuhai Bi⁶, Yuanyuan Zhang^{7

8

9

10}, Ronghua Jin^{11

12

13

14}, Rui Song^{15

16

17}

Affiliations

¹ National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
² Beijing Institute of Infectious Diseases, Beijing, 100015, China.
³ Beijing Ditan Hospital, National Center for Infectious Diseases, Capital Medical University, Beijing, 100015, China.
⁴ Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China.
⁵ Beijing Haidian Hospital, Beijing Haidian Section of Peking University Third Hospital, Beijing, 100191, China.
⁶ Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
⁷ National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China. zhangyuanyuan@ccmu.edu.cn.
⁸ Beijing Institute of Infectious Diseases, Beijing, 100015, China. zhangyuanyuan@ccmu.edu.cn.
⁹ Beijing Ditan Hospital, National Center for Infectious Diseases, Capital Medical University, Beijing, 100015, China. zhangyuanyuan@ccmu.edu.cn.
¹⁰ Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China. zhangyuanyuan@ccmu.edu.cn.
¹¹ National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China. ronghuajin@ccmu.edu.cn.
¹² Beijing Institute of Infectious Diseases, Beijing, 100015, China. ronghuajin@ccmu.edu.cn.
¹³ Beijing Ditan Hospital, National Center for Infectious Diseases, Capital Medical University, Beijing, 100015, China. ronghuajin@ccmu.edu.cn.
¹⁴ Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China. ronghuajin@ccmu.edu.cn.
¹⁵ National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China. songruii@hotmail.com.
¹⁶ Beijing Institute of Infectious Diseases, Beijing, 100015, China. songruii@hotmail.com.
¹⁷ Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, 100015, China. songruii@hotmail.com.

^# Contributed equally.

PMID: 40591137
DOI: 10.1007/s15010-025-02578-w

Abstract

Introduction: Respiratory infections re-emerge unpredictably. Rapid pathogen identification is crucial for effective targeted therapy.

Methods: From November 15, 2023, to December 15, 2023, 574 respiratory tract samples (nasopharyngeal and oropharyngeal swabs) were collected at Beijing Ditan Hospital and Beijing Haidian Hospital. Targeted next-generation sequencing (tNGS) was further used to examine the respiratory samples identified as unfavorable by quantitative real-time PCR (qPCR).

Results: Using qPCR testing, 368 out of 574 samples (64.1%) were positive, while 206 samples (35.9%) showed no pathogen. TNGS further found that 167 out of these 206 cases (81.1%) had pathogens detected, with 58 different pathogens identified. The most frequent viruses, bacteria, and fungi were H3N2 (n = 73), Streptococcus pneumoniae (S. pneumoniae) (n = 18), Staphylococcus aureus (S. aureus) (n = 18), and Candida albicans (C. albicans) (n = 17). There were 102 cases of mixed infections, among which H3N2 appeared most frequently (51/102, 50%), and coinfections often involved Human betaherpesvirus 7 and S. aureus. In 20 cases where antibiotic resistance genes (ARGs) were detected, four were infected with H3N2. Among these, TEM and tetB were associated with Acinetobacter baumannii, APH was associated with Stenotrophomonas maltophilia, and the remaining resistance genes were linked to S. pneumoniae.

Conclusion: TNGS is more sensitive than qPCR for detecting pathogens, which is crucial for identifying prevalent and harmful ones like H3N2, S. pneumoniae, and S. aureus. Its integration into routine clinical testing is recommended, though more research is needed for clear guidelines.

Keywords: Antibiotic resistance genes; H3N2; Respiratory tract infections; Targeted next-generation sequencing.

Abstract

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