Influenza and other respiratory viruses · 2026
Use of Metagenomic Next-Generation Sequencing to Investigate Respiratory Pathogens in Influenza-Negative Nasopharyngeal Swabs From Cameroon's National Influenza Surveillance.
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Fatawou Modiyinji Abdou, Pierre Cappy, Arnaud Ly, Justine Cheval, Henri Moumbeket-Yifomnjou Moise, Toutou Tsafack Desmon, Ibrahim Touoyem, Ripa Njankouo, Gwladys Monamele Chavely, Richard Njouom
Аннотация
BACKGROUND: In Cameroon, national influenza surveillance identifies influenza-positive cases, but the causes of non-influenza ARIs remain poorly characterized. We applied metagenomic next-generation sequencing (mNGS) to investigate respiratory pathogens in influenza-negative nasopharyngeal swabs collected through national influenza surveillance.
METHODS: We analyzed 199 influenza-negative nasopharyngeal swabs collected between September and December 2023 from patients with influenza-like illness or severe acute respiratory infection. Mixed DNA/RNA libraries were prepared and sequenced on an Illumina NovaSeq platform. Taxonomic classification was performed using METAMIC (v2.0) and SPAdes (V3.15.5) bioinformatic pipelines.
RESULTS: Viral sequences were detected in 120/199 samples (60.3%, 95% CI: 53.5%, 67.1%), representing 15 viral species. The most frequent viruses were rhinovirus (43/131), respiratory syncytial virus (33/131), influenza virus, parainfluenza virus and enterovirus. Two non-respiratory viruses (Rotavirus and Papillomavirus) were also detected. Sequencing generated an average of 26.1 million reads per sample, with a mean depth of 870.3 per sample and an average genomic coverage of 82%. Eleven viral co-infections were identified. Several bacterial and fungal genera were also detected.
CONCLUSION: Our study shows that mNGS provides a powerful, unbiased approach for characterizing respiratory pathogens in Cameroon, revealing substantial viral diversity among influenza-negative ARI cases. However, cost and bioinformatics capacity remain major barriers to routine implementation. Longitudinal studies and clinical correlation are needed to strengthen interpretation and guide integration of mNGS into national surveillance.
METHODS: We analyzed 199 influenza-negative nasopharyngeal swabs collected between September and December 2023 from patients with influenza-like illness or severe acute respiratory infection. Mixed DNA/RNA libraries were prepared and sequenced on an Illumina NovaSeq platform. Taxonomic classification was performed using METAMIC (v2.0) and SPAdes (V3.15.5) bioinformatic pipelines.
RESULTS: Viral sequences were detected in 120/199 samples (60.3%, 95% CI: 53.5%, 67.1%), representing 15 viral species. The most frequent viruses were rhinovirus (43/131), respiratory syncytial virus (33/131), influenza virus, parainfluenza virus and enterovirus. Two non-respiratory viruses (Rotavirus and Papillomavirus) were also detected. Sequencing generated an average of 26.1 million reads per sample, with a mean depth of 870.3 per sample and an average genomic coverage of 82%. Eleven viral co-infections were identified. Several bacterial and fungal genera were also detected.
CONCLUSION: Our study shows that mNGS provides a powerful, unbiased approach for characterizing respiratory pathogens in Cameroon, revealing substantial viral diversity among influenza-negative ARI cases. However, cost and bioinformatics capacity remain major barriers to routine implementation. Longitudinal studies and clinical correlation are needed to strengthen interpretation and guide integration of mNGS into national surveillance.