Background: Non-malarial febrile illnesses (NMFI) pose significant challenges in HIV-infected children, often leading to severe complications and increased morbidity. While traditional diagnostic approaches focus on specific pathogens, shotgun metagenomic sequencing offers a comprehensive tool to explore the microbial landscape underlying NMFI in this vulnerable population ensuring effective management.
Methods: In this study, we employed shotgun metagenomics to analyse stool samples from HIV-infected children at the Baylor Children's Clinic Uganda presenting with non-malarial febrile illness. Samples were collected and subjected to DNA extraction at the Molecular and Genomics Laboratory, Makerere University followed by shotgun metagenomics sequencing at the Chan Zuckerberg Biohub San Francisco. Bioinformatics analysis was conducted to identify and characterise the microbial composition and potential pathogenic taxa associated with NMFI using the CZID pipeline.
Results: Our findings reveal a diverse array of microbial taxa in the stool samples of HIV-infected children with NMFI. Importantly, shotgun metagenomics revealed potentially pathogenic players including Trichomonas vaginalis, Candida albicans, Giardia, and Bacteroides in stool from this patient population. This sheds light on the complexities of microbial interactions that potentially underpin non-malarial febrile illness in this group. Taxonomic profiling identified recognised pathogens and comorbidities and revealed possible new correlations with NMFI, shedding light on the pathophysiology of fever in HIV-infected children.
Conclusion: Shotgun metagenomics is a valuable method for understanding the gut microbial landscape of NMFI in HIV-infected children, providing a comprehensive approach to pathogen identification and characterisation. By revealing potential pathogenic actors beyond the fever, this work demonstrates how metagenomic sequencing may improve our knowledge of infectious illnesses in vulnerable groups and inspire targeted therapies for better clinical care and outcomes.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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