HIV-1 superinfection can occur in the presence of broadly neutralizing antibodies

Serwanga, Jennifer; Ssemwanga, Deogratius; Muganga, Michael; Nakiboneka, Ritah; Nakubulwa, Susan; Kiwuwa-Muyingo, Sylvia; Morris, Lynn; Redd, Andrew D.; Quinn, Thomas C.; Kaleebu, Pontiano; Mayanja, Yunia; Hermanus, Tandile; Ilmonen, Pauliina; Levin, Jonathan; Porcella, Stephen F.

doi:10.1016/j.vaccine.2017.11.075

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…However, there are still unknowns such as, the factors contributing to its acquisition and its incidence in the population [14,15]. The immunological responses associated with HIV-1 superinfection also lack understanding [16,17]. Finally, HIV-1 superinfections leading to rapid or, preventing disease progression is debated [18,19], These uncertainties remain because a systematic assessment of HIV-1 superinfection is challenging.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Molecular Epidemiology Screen Reveals Numerous Human Immunodeficiency Virus (HIV) Type 1 Superinfections in the Swiss HIV Cohort Study

Chaudron

Leemann

Kusejko

et al. 2022

The Journal of Infectious Diseases

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Background Studying HIV-1 superinfection is important to understand virus transmission, disease progression, and vaccine design. But detection remains challenging, with low sampling frequencies, and insufficient longitudinal samples. Methods Using the Swiss HIV Cohort Study(SHCS), we developed a molecular epidemiology screening for superinfections. A phylogeny built from 22,243 HIV-1 partial-polymerase sequences was used to identify potential superinfections among 4,575 SHCS participants with longitudinal sequences. A subset of potential superinfections was tested by near-full-length viral genome sequencing (NFVGS) of bio-banked plasma samples. Results Based on phylogenetic and distance criteria, 325 potential HIV-1 superinfections were identified and categorised by their likelihood of being detected as superinfection due to sample misidentification. NFVGS was performed for 128 potential superinfections: Of these, fifty-two were confirmed by NFVGS, 15 were not confirmed, and for 61 sampling did not allow for confirming or rejecting superinfection because the sequenced samples did not include the relevant time points causing the superinfection signal in the original screen. Thus, NFVGS could support 52/67 adequately sampled potential superinfections. Conclusions This cohort-based molecular approach identified, to our knowledge, the largest population of confirmed superinfections, showing that, while rare with a prevalence of 1-7%, superinfections are not negligible events.

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Section: Introductionmentioning

confidence: 99%

A Systematic Molecular Epidemiology Screen Reveals Numerous Human Immunodeficiency Virus (HIV) Type 1 Superinfections in the Swiss HIV Cohort Study

Chaudron

Leemann

Kusejko

et al. 2022

The Journal of Infectious Diseases

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“…Primary infection incidence exceeds superinfection incidence in certain cohorts but not others [8,10]. Immune factors may protect against or enhance the risk of superinfection [11,12], though even the presence of broadly neutralizing antibodies may not protect against superinfection [13].…”

Section: Introductionmentioning

confidence: 99%

Model-based estimation of superinfection prevalence from limited datasets

Reeves

Magaret

Greninger

et al. 2018

J. R. Soc. Interface.

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Humans can be infected sequentially by different strains of the same virus. Estimating the prevalence of so-called 'superinfection' for a particular pathogen is vital because superinfection implies a failure of immunologic memory against a given virus despite past exposure, which may signal challenges for future vaccine development. Increasingly, viral deep sequencing and phylogenetic inference can discriminate distinct strains within a host. Yet, a population-level study may misrepresent the true prevalence of superinfection for several reasons. First, certain infections such as herpes simplex virus (HSV-2) only reactivate single strains, making multiple samples necessary to detect superinfection. Second, the number of samples collected in a study may be fewer than the actual number of independently acquired strains within a single person. Third, detecting strains that are relatively less abundant can be difficult, even for other infections such as HIV-1 where deep sequencing may identify multiple strains simultaneously. Here we develop a model of superinfection inspired by ecology. We define an infected individual's richness as the number of infecting strains and use ecological evenness to quantify the relative strain abundances. The model uses an EM methodology to infer the true prevalence of superinfection from limited clinical datasets. Simulation studies with known true prevalence are used to contrast our EM method to a standard (naive) calculation. While varying richness, evenness and sampling we quantify the accuracy and precision of our method. The EM method outperforms in all cases, particularly when sampling is low, and richness or unevenness is high. Here, sensitivity to our assumptions about clinical data is considered. The simulation studies also provide insight into optimal study designs; estimates of prevalence improve equally by enrolling more participants or gathering more samples per person. Finally, we apply our method to data from published studies of HSV-2 and HIV-1 superinfection.

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