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Kanyerezi, S.

Publications and source records attributed to Kanyerezi, S..

2 recordsLinked to original sources

Comparative genomic analysis of Mycobacterium tuberculosis reveals evolution and genomic instability within Uganda I sub-lineage

IntroductionTuberculosis (TB) is the leading cause of morbidity and mortality globally, responsible for an estimated annual 10.0 million new cases and 1.3 million deaths among infectious diseases with Africa contributing a quarter of these cases in 2019. Classification of Mycobacterium tuberculosis (MTB) strains is important in understanding their geographical predominance and pathogenicity. Different studies have gone ahead to classify MTB using different methods. Some of these include; RFLP, spoligotyping, MIRU-VNTR and SNP set based phylogeny. The SNP set based classification has been found to be in concordance with the region of difference (RD) analysis of MTB complex classification system. In Uganda, the most common cause of pulmonary tuberculosis (PTB) is Uganda genotype of MTB and accounts for up to 70 % of isolates. MethodsSequenced MTB genome samples were retrieved from NCBI and others from local sequencing projects. The genomes were subjected to snippy (a rapid haploid variant calling and core genome alignment) to call variants and annotate them. Outputs from snippy were used to classify the isolates into Uganda genotypes and Non Ugandan genotypes based on 62 SNP set. The Ugandan genotype isolates were later subjected to 413 SNP set and then to a pan genome wide association analysis. Results6 Uganda genotype isolates were found not to classify as either Uganda I or II genotypes based on the 62 SNP set. Using the 413 SNP set, the 6 Uganda genotype isolates were found to have only one SNP out of the 7 SNPs that classify the Uganda I genotypes. They were also found to have both missense and frameshift mutations within the ctpH gene whereas the rest of Uganda I that had a mutation within this gene, was a missense. ConclusionAmong the Uganda genotypes genomes, Uganda I genomes are unstable. We used publicly available datasets to perform analysis like mapping, variant calling, mixed infection, pan-genome analysis to investigate and compare evolution of the Ugandan genotype.

bioinformatics

Transcriptomic dysregulations associated with SARS-CoV-2 infection in human nasopharyngeal and peripheral blood mononuclear cells

IntroductionOver 24 million people have been infected globally with the novel coronavirus, SARS-CoV-2, with more than 820,000 succumbing to the resulting COVID-19 disease as of the end of August 2020. The molecular mechanisms underlying the pathogenesis of the disease are not completely elucidated. Thus, we aim to understand host response to SARS-CoV-2 infection by comparing samples collected from two distinct compartments (infection site and blood), obtained from COVID-19 subjects and healthy controls. MethodsWe used two publicly available gene expression datasets generated via RNA sequencing in two different samples; nasopharyngeal swabs and peripheral blood mononuclear cells (PBMCs). We performed a differential gene expression analysis between COVID-19 subjects and healthy controls in the two datasets and then functionally profiled their differentially expressed genes (DEGs). The genes involved in innate immunity were also determined. ResultsWe found a clear difference in the host response to SARS-CoV-2 infection between the two sample groups. In COVID-19 subjects, the nasopharyngeal sample group indicated upregulation of genes involved in cytokine activity and interferon signalling pathway, as well as downregulation of genes involved in oxidative phosphorylation and viral transcription. Host response in COVID-19 subjects for the PBMC group, involved upregulation of genes involved in the complement system and immunoglobulin mediated immune response. CXCL13, GABRE, IFITM3 were upregulated and HSPA1B was downregulated in COVID-19 subjects in both sample groups. ConclusionOur results indicate the host response to SARS-CoV-2 is compartmentalized and suggests potential biomarkers of response to SARS-CoV-2 infection. HighlightsO_LITranscriptomic profiling from publicly available RNA-seq count data revealed a site-specific immune response in COVID-19. C_LIO_LIHost response was found cellular-mediated in nasopharyngeal samples and humoral-mediated in PBMCs samples. C_LIO_LICXCL13, GABRE and IFITM3 commonly upregulated and HSPA1B downregulated in both sample groups highlights the potential of these molecules as markers of response to SARS-CoV-2 infection. C_LI

immunology