Use of Metagenomic Next-Generation Sequencing to Investigate Respiratory Pathogens in Influenza-Negative Nasopharyngeal Swabs From Cameroon's National Influenza Surveillance.

Authors

Modiyinji Abdou Fatawou, Cappy Pierre, Ly Arnaud, Cheval Justine, Moumbeket-Yifomnjou Moise Henri, Tsafack Desmon Toutou, Touoyem Ibrahim, Njankouo Ripa, Monamele Chavely Gwladys, Njouom Richard

Published in

Influenza and other respiratory viruses. Volume 20. Issue 10. Pages e70336.

Abstract

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.
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.
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.
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.

PMID:
42899356
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.