Measuring HLA-B allele expression across differential cell types.

Masters Degree. University of KwaZulu-Natal, Durban.Background: The human leukocyte antigen (HLA) region has shown to have the strongest disease associations and recent studies have shown that expression levels of these HLA molecules play a major role in the clinical course of diseases. Differences in the expression levels of these molecules have been found to have a major effect on their ability to present specific peptide antigens. HLA molecules are critical to the interaction between diseases and components of the immune system. Expression of such molecules, namely HLA-C and HLA-A, have been shown to associate with HIV disease outcomes. An increase in expression of HLA-C leads to protection against HIV whereas an increase in HLA-A expression leads to rapid HIV progression. Furthermore, studies have shown the region with the strongest genetic effect falls within the HLA-B gene, as determined by genome wide association studies. However, limited information is available for HLA-B allelic expression levels and the variation across differential cell types. Materials and Methods: Allelic expression levels of HLA-B were measured using cryopreserved PBMC samples from HIV negative and positive cohorts with HLA typing. Antibodies specific to the HLA-B protein were identified. The affinity of the antibodies relative to class-I alleles were determined. Based on these affinities, donors with specific alleles were selected for HLA-B cell surface measurement using the flow cytometer. mRNA levels were measured across HLA-A, -B, -C and -E genes within the following cell types T-cells, B-cells, Monocytes and NK cells. These levels and a comparison of HIV infected and uninfected mRNA levels from the same donor were measured using droplet digital PCR (ddPCR). Conclusions: Contrary to HLA-B mRNA expression levels, we find cell surface expression levels vary in an allele-specific manner. We further observed differential mRNA expression patterns for HLA-A, HLA-B, HLA-C and HLA-E across cell types. We also observed no mRNA expression variation across pre- and post- HIV samples. When comparing HLA-B mRNA and surface expression across alleles and donors no significant correlation was found. However, at an donor level, some alleles may be differentially regulated at the cell surface. This study built existing knowledge and fills in some of the gaps in knowledge surrounding HLA-B expression. We also report, for the first-time, variation in allele specific expression, variation in expression across differential cell types and lack of expression variation across pre- and post- HIV infection at the mRNA level.

Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic in southern Africa has been characterised by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, whilst the second and third waves were driven by the Beta and Delta variants, respectively1-3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng Province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, predicted to influence antibody neutralization and spike function4. Here, we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity

A year of genomic surveillance reveals how the SARS-CoV-2 pandemic unfolded in Africa.

The progression of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in Africa has so far been heterogeneous, and the full impact is not yet well understood. In this study, we describe the genomic epidemiology using a dataset of 8746 genomes from 33 African countries and two overseas territories. We show that the epidemics in most countries were initiated by importations predominantly from Europe, which diminished after the early introduction of international travel restrictions. As the pandemic progressed, ongoing transmission in many countries and increasing mobility led to the emergence and spread within the continent of many variants of concern and interest, such as B.1.351, B.1.525, A.23.1, and C.1.1. Although distorted by low sampling numbers and blind spots, the findings highlight that Africa must not be left behind in the global pandemic response, otherwise it could become a source for new variants

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Rapid Replacement of SARS-CoV-2 Variants by Delta and Subsequent Arrival of Omicron, Uganda, 2021.

Genomic surveillance in Uganda showed rapid replacement of severe acute respiratory syndrome coronavirus 2 over time by variants, dominated by Delta. However, detection of the more transmissible Omicron variant among travelers and increasing community transmission highlight the need for near-real-time genomic surveillance and adherence to infection control measures to prevent future pandemic waves

Genomic epidemiology of SARS-CoV-2 during the first four waves in Mozambique.

Mozambique reported the first case of coronavirus disease 2019 (COVID-19) in March 2020 and it has since spread to all provinces in the country. To investigate the introductions and spread of SARS-CoV-2 in Mozambique, 1 142 whole genome sequences sampled within Mozambique were phylogenetically analyzed against a globally representative set, reflecting the first 25 months of the epidemic. The epidemic in the country was marked by four waves of infection, the first associated with B.1 ancestral lineages, while the Beta, Delta, and Omicron Variants of Concern (VOCs) were responsible for most infections and deaths during the second, third, and fourth waves. Large-scale viral exchanges occurred during the latter three waves and were largely attributed to southern African origins. Not only did the country remain vulnerable to the introductions of new variants but these variants continued to evolve within the borders of the country. Due to the Mozambican health system already under constraint, and paucity of data in Mozambique, there is a need to continue to strengthen and support genomic surveillance in the country as VOCs and Variants of interests (VOIs) are often reported from the southern African region

Emergence and phenotypic characterization of the global SARS-CoV-2 C.1.2 lineage

CODE AVAILABILITY : The R and python scripts used to generate figures (excluding bar charts) in this paper are available at https://github.com/NICD-CRDM/C.1.2_scripts. The Nextstrain build profile, other scripts required to run the custom pipeline, and GISAID accession identifiers for all sequences in the final tree are available at https://github.com/NICD-CRDM/ C.1.2_scripts/tree/main/Nextstrain_files. The MATLAB scripts used for microscopy are available at https://github.com/NICD-CRDM/C.1.2_scripts/tree/main/microscopy. This code is also available in Supplementary Software 1.DATA AVAILABILITY : All of the global SARS-CoV-2 genomes generated and presented in this article are publicly accessible through the GISAID9 platform (https://www.gisaid.org/), along with all other SARS-CoV-2 genomes generated by the NGS-SA. The GISAID accession identifiers of the C.1.2 sequences analyzed in this study are provided as part of Supplementary Tables 1 and 2, which also contain the metadata for the sequences. The Nextstrain build of C.1.2 and global sequences is available at https://nextstrain.org/ groups/ngs-sa/COVID19-C.1.2-2022-01-05. The GISAID accession identifiers for the full set of sequences used in this build can be accessed at https://github.com/NICD-CRDM/ C.1.2_scripts/tree/main/Nextstrain_files. The GISAID accession identifiers for the sequences used in Supp. Fig. 2a and temporal analysis can be accessed at https:// github.com/NICD-CRDM/C.1.2_scripts in the files violin_plot_IDs.xlsx and C.1.2_global_tempest.xlsx respectively. The shapefile used for South African maps in Supplementary Fig. 1 was downloaded from https://gadm.org/ (licensed for use in academic publications, see https://gadm.org/license.html) and visualised in R with ggplot2. The global map in Supplementary Fig. 1 was obtained from the rnaturalearth package (public domain, see https://docs.ropensci.org/rnaturalearth/articles/ rnaturalearth.html) and visualised with ggplot2. The data was based on sequences available on GISAID at the time.Global genomic surveillance of SARS-CoV-2 has identified variants associated with increased transmissibility, neutralization resistance and disease severity. Here we report the emergence of the PANGO lineage C.1.2, detected at low prevalence in South Africa and eleven other countries. The initial C.1.2 detection is associated with a high substitution rate, and includes changes within the spike protein that have been associated with increased transmissibility or reduced neutralization sensitivity in SARS-CoV-2 variants of concern or variants of interest. Like Beta and Delta, C.1.2 shows significantly reduced neutralization sensitivity to plasma from vaccinees and individuals infected with the ancestral D614G virus. In contrast, convalescent donors infected with either Beta or Delta show high plasma neutralization against C.1.2. These functional data suggest that vaccine efficacy against C.1.2 will be equivalent to Beta and Delta, and that prior infection with either Beta or Delta will likely offer protection against C.1.2.The Strategic Health Innovation Partnerships Unit of the South African Medical Research Council, with funds received from the South African Department of Science and Innovation. Sequencing activities for the different sequencing hubs were provided by a conditional grant from the South African National Department of Health as part of the emergency COVID-19 response, a cooperative agreement between the National Institute for Communicable Diseases of the National Health Laboratory Service and the United States Centers for Disease Control and Prevention; the African Society of Laboratory Medicine (ASLM) and Africa Centers for Disease Control and Prevention through a sub-award from the Bill and Melinda Gates Foundation; the UK Foreign, Commonwealth and Development Office and Wellcome; the South African Medical Research Council; the UK Department of Health and Social Care and managed by the Fleming Fund and performed under the auspices of the SEQAFRICA project; German Federal Ministry of Education and Research for the African Network for Improved Diagnostics, Epidemiology and Management of common infectious Agents (ANDEMIA). This study was supported by the Bill and Melinda Gates award, National Institutes of Health, South African Medical Research Council and National Institutes of Health. Hyrax Biosciences’ Exatype platform was supported by the South African Medical Research Council with funds received from the Department of Science and Innovation.http://www.nature.com/naturecommunicationsam2023Medical VirologyVeterinary Tropical DiseasesSDG-03:Good heatlh and well-bein

Emergence of SARS-CoV-2 Omicron lineages BA.4 and BA.5 in South Africa

DATA AVAILABILITY : All of the SARS-CoV-2 genomes generated and presented in this article are publicly accessible through the GISAID platform (https://www.gisaid.org/). The GISAID accession identifiers of the sequences analyzed in this study are provided as part of Supplementary Table 1. Other raw data for this study are provided as a supplementary dataset at https://github.com/krisp-kwazulu-natal/SARSCoV2_South_Africa_Omicron_BA4_BA5. The reference SARS-CoV-2 genome (MN908947.3) was downloaded from the National Center for Biotechnology Information database (https://www.ncbi.nlm.nih.gov/).CODE AVAILABILITY : All custom scripts to reproduce the analyses and figures presented in this article are available at https://github.com/krisp-kwazulu-natal/ SARSCoV2_South_Africa_Omicron_BA4_BA5.Three lineages (BA.1, BA.2 and BA.3) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant of concern predominantly drove South Africa’s fourth Coronavirus Disease 2019 (COVID-19) wave. We have now identified two new lineages, BA.4 and BA.5, responsible for a fifth wave of infections. The spike proteins of BA.4 and BA.5 are identical, and similar to BA.2 except for the addition of 69–70 deletion (present in the Alpha variant and the BA.1 lineage), L452R (present in the Delta variant), F486V and the wild-type amino acid at Q493. The two lineages differ only outside of the spike region. The 69–70 deletion in spike allows these lineages to be identified by the proxy marker of S-gene target failure, on the background of variants not possessing this feature. BA.4 and BA.5 have rapidly replaced BA.2, reaching more than 50% of sequenced cases in South Africa by the first week of April 2022. Using a multinomial logistic regression model, we estimated growth advantages for BA.4 and BA.5 of 0.08 (95% confidence interval (CI): 0.08–0.09) and 0.10 (95% CI: 0.09–0.11) per day, respectively, over BA.2 in South Africa. The continued discovery of genetically diverse Omicron lineages points to the hypothesis that a discrete reservoir, such as human chronic infections and/or animal hosts, is potentially contributing to further evolution and dispersal of the virus.The South African Medical Research Council (SAMRC) with funds received from the National Department of Health. Sequencing activities for the National Institute for Communicable Diseases (NICD) are supported by a conditional grant from the South African National Department of Health as part of the emergency COVID-19 response; a cooperative agreement between the NICD of the NHLS and the US Centers for Disease Control and Prevention (CDC) (U01IP001048 and 1 NU51IP000930); the African Society of Laboratory Medicine (ASLM) and Africa Centers for Disease Control and Prevention through a sub-award from the Bill and Melinda Gates Foundation (grant number INV-018978); the UK Foreign, Commonwealth and Development Office and Wellcome (221003/Z/20/Z); and the UK Department of Health and Social Care, managed by the Fleming Fund and performed under the auspices of the SEQAFRICA project. This research was also supported by the Coronavirus Aid, Relief, and Economic Security Act (CARES ACT) through the CDC and COVID International Task Force (ITF) funds through the CDC under the terms of a subcontract with the African Field Epidemiology Network (AFENET) (AF-NICD-001/2021). Sequencing activities at KRISP and the Centre for Epidemic Response and Innovation are supported, in part, by grants from the World Health Organization, the Rockefeller Foundation (HTH 017), the Abbott Pandemic Defense Coalition (APDC), the US National Institutes of Health (U01 AI151698) for the United World Antivirus Research Network (UWARN) and the INFORM Africa project through IHVN (U54 TW012041) and the South African Department of Science and Innovation (SA DSI) and the SAMRC under the BRICS JAF (2020/049). Sequencing at the Botswana Harvard AIDS Institute Partnership was supported by funding from the Bill and Melinda Gates Foundation, the Foundation for Innovation in Diagnostics, the National Institutes of Health Fogarty International Centre (3D43TW009610-09S1) and the HHS/NIH/ National Institute of Allergy and Infectious Diseases (NIAID) (5K24AI131928-04 and 5K24AI131924-04).http://www.nature.com/naturemedicineam2023Medical VirologySDG-03:Good heatlh and well-bein