PARV4 prevalence, phylogeny, immunology and coinfection with HIV, HBV and HCV in a multicentre African cohort
- Colin P. Sharp ,
- William F. Gregory ,
- Louise Hattingh ,
- Amna Malik ,
- Emily Adland ,
- Samantha Daniels ,
- Anriette van Zyl ,
- Jonathan M. Carlson ,
- Susan Wareing ,
- Anthony Ogwu ,
- Roger Shapiro ,
- Lynn Riddell ,
- Fabian Chen ,
- Thumbi Ndung'u ,
- Philip J.R. Goulder ,
- Paul Klenerman ,
- Peter Simmonds ,
- Pieter Jooste ,
- Philippa C. Matthews
Wellcome Open Research | , Vol 2: pp. 26
Background: The seroprevalence of human parvovirus-4 (PARV4) varies considerably by region. In sub-Saharan Africa, seroprevalence is high in the general population, but little is known about the transmission routes or the prevalence of coinfection with blood-borne viruses, HBV, HCV and HIV. Methods: To further explore the characteristics of PARV4 in this setting, with a particular focus on the prevalence and significance of coinfection, we screened a cohort of 695 individuals recruited from Durban and Kimberley (South Africa) and Gaborone (Botswana) for PARV4 IgG and DNA, as well as documenting HIV, HBV and HCV status. Results: Within these cohorts, 69% of subjects were HIV-positive. We identified no cases of HCV by PCR, but 7.4% were positive for HBsAg. PARV4 IgG was positive in 42%; seroprevalence was higher in adults (69%) compared to children (21%) (p<0.0001) and in HIV-positive (52%) compared to HIV-negative individuals (24%) (p<0.0001), but there was no association with HBsAg status. We developed an on-line tool to allow visualization of coinfection data (https://purl.oclc.org/coinfection-viz (opens in new tab)). We identified five subjects who were PCR-positive for PARV4 genotype-3. Ex vivo CD8+ T cell responses spanned the entire PARV4 proteome and we propose a novel HLA-B*57:03-restricted epitope within the NS protein. Conclusions: This characterisation of PARV4 infection provides enhanced insights into the epidemiology of infection and co-infection in African cohorts, and provides the foundations for planning further focused studies to elucidate transmission pathways, immune responses, and the clinical significance of this organism.
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PhyloD
25 3 月, 2016
Machine learning tools for modeling viral adaptation to host immune responses.