TNFAIP3-interacting protein 1 polymorphisms and their association with symptomatic human respiratory syncytial virus infection and bronchiolitis in infants younger than one year from South Africa: A case-control study

OBJECTIVES

This study analyzed the association of TNFAIP3-interacting protein 1 (TNIP1) polymorphisms with the symptomatic human respiratory syncytial virus (HRSV) infection and bronchiolitis in infants.

METHODS

A case-control study was conducted involving 129 hospitalized infants with symptomatic HRSV infection (case group) and 161 healthy infants (control group) in South Africa (2016-2018). Six TNIP1 polymorphisms (rs869976, rs4958881, rs73272842, rs3792783, rs17728338, and rs999011) were genotyped. Genetic associations were evaluated using logistic regression adjusted by age and gender.

RESULTS

Both rs73272842 G and rs999011 C alleles were associated with reduced odds for symptomatic HRSV infection (adjusted odd ratio [aOR] = 0.68 [95% confidence interval {CI} = 0.48-0.96] and aOR = 0.36 [95% CI = 0.19-0.68], respectively] and bronchiolitis (aOR = 0.71 [95% CI = 0.50-1.00] and aOR = 0.38 [95% CI = 0.22-0.66], respectively). The significance of these associations was validated using the BCa Bootstrap method (P <0.05). The haplotype GC (composed of rs73272842 and rs999011) was associated with reduced odds of symptomatic HRSV infection (aOR = 0.53 [95% CI = 0.37-0.77]) and bronchiolitis (aOR = 0.62 [95% CI = 0.46-0.84]), which were validated by the BCa Bootstrap method (P = 0.002 for both).

CONCLUSION

TNIP1 rs73272842 G allele and rs999011 C allele were associated with reduced odds of symptomatic HRSV infection and the development of bronchiolitis in infants, suggesting that TNIP1 polymorphisms could impact susceptibility to HRSV illness.

Correction: Subramoney et al. Molecular Epidemiology of SARS-CoV-2 during Five COVID-19 Waves and the Significance of Low-Frequency Lineages. Viruses 2023, 15, 1194

In the original publication [...].

SARS-CoV-2 spike protein diversity at an intra-host level, among SARS-CoV-2 infected individuals in South Africa, 2020 to 2022

Intra-host diversity studies are used to characterise the mutational heterogeneity of SARS-CoV-2 infections in order to understand the impact of virus-host adaptations. This study investigated the frequency and diversity of the spike (S) protein mutations within SARS-CoV-2 infected South African individuals. The study included SARS-CoV-2 respiratory samples, from individuals of all ages, received at the National Health Laboratory Service at Charlotte Maxeke Johannesburg Academic hospital, Gauteng, South Africa, from June 2020 to May 2022. Single nucleotide polymorphism (SNP) assays and whole genome sequencing were performed on a random selection of SARS-CoV-2 positive samples. The allele frequency (AF) was determined using TaqMan Genotyper software for SNP PCR analysis and galaxy.eu for analysis of FASTQ reads from sequencing. The SNP assays identified 5.3% (50/948) of Delta cases with heterogeneity at delY144 (4%; 2/50), E484Q (6%; 3/50), N501Y (2%; 1/50) and P681H (88%; 44/50), however only heterogeneity for E484Q and delY144 were confirmed by sequencing. From sequencing we identified 9% (210/2381) of cases with Beta, Delta, Omicron BA.1, BA.2.15, and BA.4 lineages that had heterogeneity in the S protein. Heterogeneity was primarily identified at positions 19 (1.4%) with T19IR (AF 0.2-0.7), 371 (92.3%) with S371FP (AF 0.1-1.0), and 484 (1.9%) with E484AK (0.2-0.7), E484AQ (AF 0.4-0.5) and E484KQ (AF 0.1-0.4). Mutations at heterozygous amino acid positions 19, 371 and 484 are known antibody escape mutations, however the impact of the combination of multiple substitutions identified at the same position is unknown. Therefore, we hypothesise that intra-host SARS-CoV-2 quasispecies with heterogeneity in the S protein facilitate competitive advantage of variants that can completely/partially evade host's natural and vaccine-induced immune responses.

Molecular Epidemiology of SARS-CoV-2 during Five COVID-19 Waves and the Significance of Low-Frequency Lineages

SARS-CoV-2 lineages and variants of concern (VOC) have gained more efficient transmission and immune evasion properties with time. We describe the circulation of VOCs in South Africa and the potential role of low-frequency lineages on the emergence of future lineages. Whole genome sequencing was performed on SARS-CoV-2 samples from South Africa. Sequences were analysed with Nextstrain pangolin tools and Stanford University Coronavirus Antiviral & Resistance Database. In 2020, 24 lineages were detected, with B.1 (3%; 8/278), B.1.1 (16%; 45/278), B.1.1.348 (3%; 8/278), B.1.1.52 (5%; 13/278), C.1 (13%; 37/278) and C.2 (2%; 6/278) circulating during the first wave. Beta emerged late in 2020, dominating the second wave of infection. B.1 and B.1.1 continued to circulate at low frequencies in 2021 and B.1.1 re-emerged in 2022. Beta was outcompeted by Delta in 2021, which was thereafter outcompeted by Omicron sub-lineages during the 4th and 5th waves in 2022. Several significant mutations identified in VOCs were also detected in low-frequency lineages, including S68F (E protein); I82T (M protein); P13L, R203K and G204R/K (N protein); R126S (ORF3a); P323L (RdRp); and N501Y, E484K, D614G, H655Y and N679K (S protein). Low-frequency variants, together with VOCs circulating, may lead to convergence and the emergence of future lineages that may increase transmissibility, infectivity and escape vaccine-induced or natural host immunity.

Hepatitis B Virus Immunity Gap: A Six-Year Laboratory Data Review of Hepatitis B Serological Profiles in Gauteng Province, South Africa

Background

In 1995, the hepatitis B vaccine in South Africa was incorporated into the childhood expanded programme of immunization. We report on immunity gaps of laboratory-based hepatitis B virus (HBV) among patients in public facilities in Gauteng Province from 1st January 2014 to 31st December 2019. Methodology. We analyzed HBV serological data extracted from the National Health Laboratory Services Central Data Warehouse (NHLS CDW). A descriptive analysis was performed for hepatitis B surface antigen (HBsAg), antibodies to HBV core (anti-HBc) total, anti-HBc IgM, and antibodies to HBV surface antigen (anti-HBs) according to annual distribution, age groups, and sex.

Results

The HBsAg positivity rate was 7.0% (75,596/1,095,561; p=0.001): 7.4% (96,532/944,077) in the 25 years and over age group and 4.0% (358/9,268 and 325/10,864) in the under 5 and 13-24 year age groups. The positivity rates of the other HBV serological markers were as follows: anti-HBc total was 37.0% (34,377/93,711; p  <  0.001), anti-HBc IgM was 2.4% (5,661/239,237; p=0.05), and anti-HBs was 37.0% (76,302/206,138; p ≤ 0.001). Naturally acquired HBV immunity was detected in 25.7% (11,188/43,536) of patients in the 25 years and over age group, and 9.7% and 8.2% (113/1,158 and 541/6,522) among those under 5 years and 13-24 year age group, respectively (p  <  0.001). Vaccine-induced immunity was 56.6% (656/1,158) in children under 5 years and 10.2% (4,425/43,536) among those 25 years and above (p  <  0.001). Fifty-six percent (29,404/52,581) of patients were HBV seronegative; predominantly among patients in the 13-24 year age group (60.6%; (3,952/6,522)) and 25 years and over (56.3% (24,524/43,536)) (p=<0.001).

Conclusion

The HBV infection seroprevalence remains high in South Africa, with Gauteng province having high intermediate endemicity. However, the HBV immunity gap has shifted from younger children to older children and adults.

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

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.

Identification of SARS-CoV-2 Omicron variant using spike gene target failure and genotyping assays, Gauteng, South Africa, 2021

The circulation of Omicron BA.1 led to the rapid increase in severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) cases in South Africa in November 2021, which warranted the use of more rapid detection methods. We, therefore, assessed the ability to detect Omicron BA.1 using genotyping assays to identify specific mutations in SARS‐CoV‐2 positive samples, Gauteng province, South Africa. The TaqPath™ COVID‐19 real‐time polymerase chain reaction assay was performed on all samples selected to identify spike gene target failure (SGTF). SARS‐CoV‐2 genotyping assays were used for the detection of del69/70 and K417N mutation. Whole‐genome sequencing was performed on a subset of genotyped samples to confirm these findings. Of the positive samples received, 11.0% (175/1589) were randomly selected to assess if SGTF and genotyping assays, that detect del69/70 and K417N mutations, could identify Omicron BA.1. We identified SGTF in 98.9% (173/175) of samples, of which 88.0% (154/175) had both the del69/70 and K417N mutation. The genotyped samples (45.7%; 80/175) that were sequenced confirmed Omicron BA.1 (97.5%; 78/80). Our data show that genotyping for the detection of the del69/70 and K417N coupled with SGTF is efficient to exclude Alpha and Beta variants and rapidly detect Omicron BA.1. However, we still require assays for the detection of unique mutations that will allow for the differentiation between other Omicron sublineages. Therefore, the use of genotyping assays to detect new dominant or emerging lineages of SARS‐CoV‐2 will be beneficial in limited‐resource settings.

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

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) 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, B.1.1.529) 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, which are 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.

Human bocavirus, coronavirus, and polyomavirus detected among patients hospitalised with severe acute respiratory illness in South Africa, 2012 to 2013

AIM

To investigate the prevalence of human bocavirus (hBoV), human coronaviruses (hCoV), and human polyomaviruses (hPyV) among patients with severe acute respiratory illness (SARI), in South Africa.

METHODS

The study included 680 South African patients randomly selected in age-defined categories from hospitalised patients enrolled through SARI surveillance during 2012 to 2013. A multiplex reverse transcription real-time polymerase chain reaction assay was used to detect hBoV; hCoV-OC43, hCoV-229E, hCoV-NL63, and hCoV-HKU1; and Washington University hPyV (hPyV-WU) and Karolinska Insitute hPyV (hPyV-KI), in respiratory tract specimens collected from patients with SARI. All respiratory specimens from patients enrolled through SARI surveillance were also routinely tested by multiplex reverse transcription real-time polymerase chain reaction for adenovirus; enterovirus; human metapneumovirus; parainfluenza virus types 1, 2, and 3; respiratory syncytial virus; rhinovirus; influenza A, and influenza B.

RESULTS

Human bocavirus, hCoV-229E, and hPyV-WU were detected in 3.7% (25/680), 4.1% (28/680), and 4.1% (28/680) of respiratory specimens, respectively. All other viruses were detected in <2% of specimens. Rhinovirus was the most common coinfecting virus (21.4%-60.7%), followed by adenovirus (21.4%-39.3%), and respiratory syncytial virus (10.7%-24.0%). Testing for the additional viruses (hBoV, hCoV, and hPyV) decreased the number of specimens that initially tested negative by 2.9% (20/680).

CONCLUSION

Inclusion of laboratory tests for hBoV, hCoV-229E, and hPyV-WU in differential testing algorithms for surveillance and diagnostics for suspected cases of respiratory illness of unknown cause may improve our understanding of the etiology of SARI, especially in a country like South Africa with a high number of immune compromised persons.