Since the COVID-19 outbreak emerged, SARS-CoV-2 has continuously evolved into variants with underlying mutations associated with increased transmissibility, potential escape from neutralizing antibodies, and disease severity. The SARS-CoV-2 pandemic in South Africa has been characterized by periods of infections with four major epidemic waves. To determine whether the variants driving the epidemic waves at the national level were also driving the epidemic waves at the local level, we performed analysis of a total of 1287 samples from qPCR confirmed SARS-CoV-2 positive individuals. The samples were subjected to viral RNA extraction, genomic amplification, and sequencing. Variant assignment of the viral sequences and mutation identification were conducted using PANGOLIN and SARS-CoV-2 genome annotator, respectively. Our analysis revealed that during the initial part of the first wave, B.1, B.1.1, B.1.1.53, B.1.1.448 and B.1.237 circulated in the Free State province, followed by Beta variant, B.1.351 later in the wave. Although most of the initially detected variants disappeared during the second wave, the Beta variant, B.1.351, persisted. Early in the third wave, the Beta variant, B.1.351, predominated but was replaced by the Delta sub-lineage, AY.45. The fourth wave was characterized by unique emergence of the Omicron sub-variant, BA.1. The data further indicates that SARS-CoV-2 variants driving the epidemic waves in the Free State at the local level correlated with the ones driving the epidemic waves at the national level. Findings from this study highlight the importance of continued genomic surveillance and monitoring of the circulating SARS-CoV-2 variants to inform public health efforts and ensure adequate control of the ongoing pandemic.
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to be a significant public health challenge globally. SARS-CoV-2 is a novel virus, and the understanding of what constitutes expressed RNAseq variants in healthy, convalescent, severe, moderate and to those admitted at the Intensive Care Unit (ICU) is yet to be presented. We set to characterize the different expressed RNAseq variants in healthy, severe, moderate, ICU, and convalescent individuals. Materials and methods: The bulk RNA sequencing data with identifier PRJNA639275 was download from Sequence Reads Archive (SRA). The individuals were divided into: (i) healthy, n=34, severe, n=16, ICU, n=8, moderate, n=8, and convalescent, n=2. Fastqc version 0.11.9 and Cutadapt version 3.7 was used to asses the reads quality and to perform adapter trimming respectively. STAR was using to align reads to the reference genome and GATK best practice was followed to call variants using rnavar pipeline, part of the nf-core pipelines. Results: Our analysis demonstrated that convalescent, moderate, severe and those admitted to the ICU are characterized by different sets of unique RNAseq variants. The data shows that the individuals who recover from SARS-CoV-2 infection have the same set of expressed variants as in the healthy controls. We showed that the healthy and SARS-CoV-2 infected individuals display different sets of expressed varinats which is characteristic of the patient phenotype. Conclusion: The individuals with severe, moderate, those admitted at the ICU, and convalescent individuals display a unique set of variants. The findings in this study will inform the test kit development and SARS-CoV-2 patients classification to enhance management and control of SARS-CoV-2 infection in our population. Key words: RNAseq, variants, SARS-CoV-2, severe, ICU, moderate
Background: Proteome profile changes post-severe acute respiratory syndrome coronavirus 2 (post-SARS-CoV-2) infection in different body sites of humans remains an active scientific investigation whose solutions stand a chance of providing more information on what constitutes SARS-CoV-2 pathogenesis. While proteomics has been used to understand SARS-CoV-2 pathogenesis, there are limited data about the status of proteome profile in different human body sites infected by sarscov2 virus. To bridge this gap, our study aims to profile the proteins secreted in urine, bronchoalveolar lavage fluid (BALF), gargle solution, and nasopharyngeal samples and assess the proteome differences in these body samples collected from SARS-CoV-2-positive patients. Materials and methods: We downloaded publicly available proteomic data from (https://www.ebi.ac.uk/pride/). The data we downloaded had the following identifiers: i) PXD019423, n=3 from Charles Tanford Protein Center in Germany. ii) PXD018970, n=15 from Beijing Proteome Research Centre, China. iii)PXD022085, n=5 from Huazhong University of Science and Technology, China, and iv) PXD022889, n=18 from Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905 USA. MaxQuant was used for the peptide spectral matching using human and SARS-CoV-2 downloaded from UniProt database (access date 13th October 2021). Results: The individuals infected with SARS-CoV-2 viruses displayed a different proteome diversity from the different body sites we investigated. Overally, we identified 1809 proteins across the four different sample types we compared. Urine and BALF samples had significantly more abundant SARS-CoV-2 proteins than the other body sites we compared. Urine samples had 257(33.7%) unique proteins followed by nasopharyngeal with 250(32.8%) unique proteins. Garlge solution and BALF had 38(5%) and 73(9.6%) unique proteins respectively. Conclusions: Urine, gargle solution, nasopharyngeal, and bronchoalveolar lavage fluid samples have different protein diversity in individuals infected with SARS-CoV-2. Moreover, our data also demonstrated that a given body site is characterized by a unique set of proteins in SARS-CoV-2 seropositive individuals. Key words: SARS-CoV-2, body sites,urine,gargle solution, BALF,nasopharyngeal
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