Biobanking in East and Central Africa: A case of the Integrated Biorepository of H3Africa Uganda

Nsubuga, Gideon; Kateete, David Patrick; Aloyo, Sharley Melissa; Kigingi, Lwanga Newton; Nasinghe, Emmanuel; Dafala, Kezimbira; Ntayi, Moses Levi; Joloba, Moses; Kamulegeya, Rogers

doi:10.12688/openresafrica.13495.1

Cited by 1 publication

(1 citation statement)

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“…Sample collection took place at Mulago hospital, other ministry of health approved COVID-|9 treatment centres and the community. The samples were processed in the Makerere University Laboratories and stored at the Integrated Biorepository of H3Africa Uganda [35]. The COVID-19 diagnosis was performed by SARS-CoV-2 detection using real-time reverse transcriptase-polymerase chain reaction (RT-PCR) assay on the gut swabs.…”

Section: Methodsmentioning

confidence: 99%

Gut microbial profiling of COVID-19 patients in Uganda

Kateete,

Lubega,

Galiwango

et al. 2024

Preprint

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Background:While COVID-19 spread globally, the role of the gut microbiota in patient outcomes has remained an area of exploration especially in resource limited settings. This study aimed to comprehensively profile the gut microbiome among Ugandan COVID-19 patients and infer potential implications.Methods:Nasopharyngeal swabs, stool, clinical and demographic data were collected from COVID-19 confirmed cases at the COVID-19 isolation and treatment centers in Kampala and Entebbe, Uganda, during the first and second waves of the pandemic in Uganda (i.e., 2020 and 2021, respectively). SARS-CoV-2 presence in the swab samples was confirmed by quantitative real-time RT-PCR assays. 16S rRNA metagenomic next-generation sequencing was performed on the DNA extracted from the stool samples, followed by bioinformatics analysis. Machine learning was used to determine microbes that were associated with disease severity.Results:We observed varied gut microbial composition between COVID-19 patients and healthy controls. Potentially pathogenic bacteria such as Klebsiella oxytoca, Salmonella enterica and Serratia marcescens had an increased presence in COVID-19 disease states, especially severe cases. Enrichment of opportunistic pathogens, such as Enterococcus species, and depletion of beneficial microbes, like Alphaproteobacteria, was observed between mild and severe cases. Machine learning identified age and microbes such as Ruminococcaceae, Bacilli, Enterobacteriales, porphyromonadaceae, and Prevotella copri as predictive of severity.Conclusion:These findings suggest that the microbiome plays a role in the dynamics of SARS-CoV-2 infection in African patients. The shift in abundance of specific microbes can moderately predict severity of COVID-19 in this population. Their direct or indirect roles in determining severity should be investigated further for potential therapeutic interventions.

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Section: Methodsmentioning

confidence: 99%