RESUMO
Background: The Basic Science Laboratory (BSL) of the Kenya Medical Research Institute/Walter Reed Project in Kisumu, Kenya addressed mass testing challenges posed by the emergent coronavirus disease 2019 (COVID-19) in an environment of global supply shortages. Before COVID-19, the BSL had adequate resources for disease surveillance and was therefore designated as one of the testing centres for COVID-19. Intervention: By April 2020, the BSL had developed stringent safety procedures for receiving and mass testing potentially infectious nasal specimens. To accommodate increased demand, BSL personnel worked in units: nucleic acid extraction, polymerase chain reaction, and data and quality assurance checks. The BSL adopted procedures for tracking sample integrity and minimising cross-contamination. Lessons learnt: Between May 2020 and January 2022, the BSL tested 63 542 samples, of which 5375 (8.59%) were positive for COVID-19; 1034 genomes were generated by whole genome sequencing and deposited in the Global Initiative on Sharing All Influenza Data database to aid global tracking of viral lineages. At the height of the pandemic (August and November 2020, April and August 2021 and January 2022), the BSL was testing more than 500 samples daily, compared to 150 per month prior to COVID-19. An important lesson from the COVID-19 pandemic was the discovery of untapped resilience within BSL personnel that allowed adaptability when the situation demanded. Strict safety procedures and quality management that are often difficult to maintain became routine. Recommendations: A fundamental lesson to embrace is that there is no 'one-size-fits-all' approach and adaptability is the key to success.
RESUMO
Background: Kenya's COVID-19 epidemic was seeded early in March 2020 and did not peak until early August 2020 (wave 1), late-November 2020 (wave 2), mid-April 2021 (wave 3), late August 2021 (wave 4), and mid-January 2022 (wave 5). Methods: Here, we present SARS-CoV-2 lineages associated with the five waves through analysis of 1034 genomes, which included 237 non-variants of concern and 797 variants of concern (VOC) that had increased transmissibility, disease severity or vaccine resistance. Results: In total 40 lineages were identified. The early European lineages (B.1 and B.1.1) were the first to be seeded. The B.1 lineage continued to expand and remained dominant, accounting for 60% (72/120) and 57% (45/79) in waves 1 and 2 respectively. Waves three, four and five respectively were dominated by VOCs that were distributed as follows: Alpha 58.5% (166/285), Delta 92.4% (327/354), Omicron 95.4% (188/197) and Beta at 4.2% (12/284) during wave 3 and 0.3% (1/354) during wave 4. Phylogenetic analysis suggests multiple introductions of variants from outside Kenya, more so during the first, third, fourth and fifth waves, as well as subsequent lineage diversification. Conclusions: The data highlights the importance of genome surveillance in determining circulating variants to aid interpretation of phenotypes such as transmissibility, virulence and/or resistance to therapeutics/vaccines.
RESUMO
BACKGROUND: There is a global increase in reports of emerging diseases, some of which have emerged as spillover events from wild animals. The spleen is a major phagocytic organ and can therefore be probed for systemic microbiome. This study assessed bacterial diversity in the spleen of wild caught small mammals so as to evaluate their utility as surveillance tools for monitoring bacteria in an ecosystem shared with humans. METHODS: Fifty-four small mammals (rodents and shrews) were trapped from different sites in Marigat, Baringo County, Kenya. To characterize their bacteriome, DNA was extracted from their spleens and the V3-V4 regions of the 16S rRNA amplified and then sequenced on Illumina MiSeq. A non-target control sample was used to track laboratory contaminants. Sequence data was analyzed with Mothur v1.35, and taxomy determined using the SILVA database. The Shannon diversity index was used to estimate bacterial diversity in each animal and then aggregated to genus level before computing the means. Animal species within the rodents and shrews were identified by amplification of mitochondrial cytochrome b (cytb) gene followed by Sanger sequencing. CLC workbench was used to assemble the cytb gene sequences, after which their phylogenetic placements were determined by querying them against the GenBank nucleotide database. RESULTS: cytb gene sequences were generated for 49/54 mammalian samples: 38 rodents (Rodentia) and 11 shrews (Eulipotyphyla). Within the order Rodentia, 21 Acomys, eight Mastomys, six Arvicanthis and three Rattus were identified. In the order Eulipotyphyla, 11 Crucidura were identified. Bacteria characterization revealed 17 phyla that grouped into 182 genera. Of the phyla, Proteobacteria was the most abundant (67.9%). Other phyla included Actinobacteria (16.5%), Firmicutes (5.5%), Chlamydiae (3.8%), Chloroflexi (2.6%) and Bacteroidetes (1.3%) among others. Of the potentially pathogenic bacteria, Bartonella was the most abundant (45.6%), followed by Anaplasma (8.0%), Methylobacterium (3.5%), Delftia (3.8%), Coxiella (2.6%), Bradyrhizobium (1.6%) and Acinetobacter (1.1%). Other less abundant (<1%) and potentially pathogenic included Ehrlichia, Rickettsia, Leptospira, Borrelia, Brucella, Chlamydia and Streptococcus. By Shannon diversity index, Acomys spleens carried more diverse bacteria (mean Shannon diversity index of 2.86, p = 0.008) compared to 1.77 for Crocidura, 1.44 for Rattus, 1.40 for Arvicathis and 0.60 for Mastomys. CONCLUSION: This study examined systemic bacteria that are filtered by the spleen and the findings underscore the utility of 16S rRNA deep sequencing in characterizing complex microbiota that are potentially relevant to one health issues. An inherent problem with the V3-V4 region of 16S rRNA is the inability to classify bacteria reliably beyond the genera. Future studies should utilize the newer long read methods of 16S rRNA analysis that can delimit the species composition.
