An uncommon case of HIV-1 transmission due to a knife fight

Inferring Viral Transmission Time from Phylogenies for Known Transmission Pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

Inferring viral transmission time from phylogenies for known transmission pairs

When the time of an HIV transmission event is unknown, methods to identify it from virus genetic data can reveal the circumstances that enable transmission. We developed a single-parameter Markov model to infer transmission time from an HIV phylogeny constructed of multiple virus sequences from people in a transmission pair. Our method finds the statistical support for transmission occurring in different possible time slices. We compared our time-slice model results to previously-described methods: a tree-based logical transmission interval, a simple parsimony-like rules-based method, and a more complex coalescent model. Across simulations with multiple transmitted lineages, different transmission times relative to the source's infection, and different sampling times relative to transmission, we found that overall our time-slice model provided accurate and narrower estimates of the time of transmission. We also identified situations when transmission time or direction was difficult to estimate by any method, particularly when transmission occurred long after the source was infected and when sampling occurred long after transmission. Applying our model to real HIV transmission pairs showed some agreement with facts known from the case investigations. We also found, however, that uncertainty on the inferred transmission time was driven more by uncertainty from time-calibration of the phylogeny than from the model inference itself. Encouragingly, comparable performance of the Markov time-slice model and the coalescent model-which make use of different information within a tree-suggests that a new method remains to be described that will make full use of the topology and node times for improved transmission time inference.

Human immunodeficiency virus transmission from a 24-year-old woman to her 78-year-old grandmother: a case report

Introduction

Since its debut recognition in 1981, human immunodeficiency virus/acquired immunodeficiency syndrome has affected over 77 million people and has resulted in premature cessation of 35.4 million lives worldwide. Commonly, human immunodeficiency virus is transmitted by sexual contact across mucosal surfaces, by sharing of injecting equipment, through contaminated blood transfusions, and by maternal–infant exposure. Nevertheless, accidental transmission incidences involving family members are rare but possible.

Case presentation

A 78-year-old woman of African descent from Mtwara Region south of Tanzania was referred to us for further evaluation and treatment. She is 30 years postmenopausal and has a 35-year history of hypertension. Her last attendance to our institute was 11 months prior the index visit and she tested negative for human immunodeficiency virus. She came with complaints of weight loss, recurrent fevers, and cough. Her hematological tests revealed leukopenia with lymphocytosis, together with a normocytic normochromic anemia. Enzyme-linked immunosorbent assay for human immunodeficiency virus was positive, and she had a CD4 count of 177 cells/µL. We went back to history taking to identify the potential source of infection. We were informed that for the past 6 months, the 78-year-old lady has been living with her unwell 24-year-old granddaughter who has been divorced. The granddaughter had a history of recurrent fevers, significant weight loss, and a suppurative skin condition. As a way to show love and care, the old lady was puncturing the suppurative lesions with bare hands; then she would suck them to clear away the discharge. We requested to see the young lady, and she tested positive for human immunodeficiency virus. Both were started on tenofovir/lamivudine/dolutegravir combination plus cotrimoxazole 960 mg. The family was in total disarray following these findings. The patient was discharged through infectious diseases department and died of Pneumocystis jirovecii pneumonia 12 weeks later.

Conclusions

Certain sociocultural norms that are believed to express love, care, and togetherness in developing rural communities, particularly Sub-Saharan Africa, have a potential of spreading human immunodeficiency virus, thus warranting prompt transformation.

Phylogenetically resolving epidemiologic linkage

Although the use of phylogenetic trees in epidemiological investigations has become commonplace, their epidemiological interpretation has not been systematically evaluated. Here, we use an HIV-1 within-host coalescent model to probabilistically evaluate transmission histories of two epidemiologically linked hosts. Previous critique of phylogenetic reconstruction has claimed that direction of transmission is difficult to infer, and that the existence of unsampled intermediary links or common sources can never be excluded. The phylogenetic relationship between the HIV populations of epidemiologically linked hosts can be classified into six types of trees, based on cladistic relationships and whether the reconstruction is consistent with the true transmission history or not. We show that the direction of transmission and whether unsampled intermediary links or common sources existed make very different predictions about expected phylogenetic relationships: (i) Direction of transmission can often be established when paraphyly exists, (ii) intermediary links can be excluded when multiple lineages were transmitted, and (iii) when the sampled individuals' HIV populations both are monophyletic a common source was likely the origin. Inconsistent results, suggesting the wrong transmission direction, were generally rare. In addition, the expected tree topology also depends on the number of transmitted lineages, the sample size, the time of the sample relative to transmission, and how fast the diversity increases after infection. Typically, 20 or more sequences per subject give robust results. We confirm our theoretical evaluations with analyses of real transmission histories and discuss how our findings should aid in interpreting phylogenetic results.

Disentangling the impact of within-host evolution and transmission dynamics on the tempo of HIV-1 evolution

OBJECTIVE

To determine how HIV-1 risk groups impact transmitted diversity and the tempo of viral evolution at a population scale.

METHODS

We investigated a set of previously described transmission chains (n = 70) using a population genetic approach, and tested whether the expected differences in proportions of multivariant transmissions are reflected by varying proportions of transmitted diversity between men having sex with men (MSM) and heterosexual (HET) subpopulations - the largest contributors to HIV spread. To assess evolutionary rate differences among the different risk groups, we compiled risk group datasets for subtypes A1, B and CRF01_AE, and directly compared the absolute substitution rate and its synonymous and non-synonymous components.

RESULTS

There was sufficient demographic signal to inform the transmission model in Bayesian evolutionary analysis by sampling trees using env data to compare the transmission bottleneck size between the MSM and HET risk groups. We found no indications for a different proportion of transmitted genetic diversity at the population level between these groups. In the direct rate comparisons between the risk groups, however, we consistently recovered a higher evolutionary rate in the male-dominated risk group compared to the HET datasets.

CONCLUSION

We find that the risk group composition affects the viral evolutionary rate and therefore potentially also the adaptation rate. In particular, risk group-specific sex ratios, and the variation in within-host evolutionary rates between men and women, impose evolutionary rate differences at the epidemic level, but we cannot exclude a role of varying transmission rates.