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. 2024 Sep 19;18(9):e0012451.
doi: 10.1371/journal.pntd.0012451. eCollection 2024 Sep.

Metagenomic next generation sequencing of plasma RNA for diagnosis of unexplained, acute febrile illness in Uganda

Affiliations

Metagenomic next generation sequencing of plasma RNA for diagnosis of unexplained, acute febrile illness in Uganda

Abraham J Kandathil et al. PLoS Negl Trop Dis. .

Abstract

Metagenomic next generation metagenomic sequencing (mNGS) has proven to be a useful tool in the diagnosis and identification of novel human pathogens and pathogens not identified on routine clinical microbiologic tests. In this study, we applied mNGS to characterize plasma RNA isolated from 42 study participants with unexplained acute febrile illness (AFI) admitted to tertiary referral hospitals in Mubende and Arua, Uganda. Study participants were selected based on clinical criteria suggestive of viral infection (i.e., thrombocytopenia, leukopenia). The study population had a median age of 28 years (IQR:24 to 38.5) and median platelet count of 114 x103 cells/mm3 (IQR:66,500 to 189,800). An average of 25 million 100 bp reads were generated per sample. We identified strong signals from diverse virus, bacteria, fungi, or parasites in 10 (23.8%) of the study participants. These included well recognized pathogens like Helicobacter pylori, human herpes virus-8, Plasmodium falciparum, Neisseria gonorrhoeae, and Rickettsia conorii. We further confirmed Rickettsia conorii infection, the cause of Mediterranean Spotted Fever (MSF), using PCR assays and Sanger sequencing. mNGS was a useful addition for detection of otherwise undetected pathogens and well-recognized non-pathogens. This is the first report to describe the molecular confirmation of a hospitalized case of MSF in sub-Saharan Africa (SSA). Further studies are needed to determine the utility of mNGS for disease surveillance in similar settings.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. KrakenUniq read classifications identified microbes across 42 study samples.
KrakenUniq read classifications for 9 identified microbes across 42 study samples are shown. The identified reads were absent in negative control (NTC), and the AcroMetrix control. Each of these microbes had significantly higher total read counts in 1–2 samples as compared to the remaining samples and controls, as determined by the Pavian z-score.

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