Hepatitis C virus dynamics among intravenous drug users suggest that an annual treatment uptake above 10% would eliminate the disease by 2030.

In Switzerland, the prevalence of hepatitis C virus (HCV) among people who inject drugs (PWID) has been decreasing owing to active harm reduction efforts and an aging population. Recent advances in HCV therapeutics may provide an opportunity to direct treatment to high-risk populations, with a goal of reducing HCV prevalence and preventing new infections. In order to guide these efforts, the current project was undertaken with the following aims: (1) to develop a simple model to estimate the number of new HCV infections using available data on PWID; (2) to examine the impact of intervention strategies (prevention and treatment) on new and total HCV infections among PWID. A dynamic HCV transmission model was used to track HCV incidence and prevalence among active PWID according to their harm reduction status. The relative impact of treating 1, 5, 10 or 15% of HCV+ PWID with new oral direct acting antivirals was considered. In 2015, there were an estimated 10 160 active PWID in Switzerland, more than 85% of whom were engaged in harm reduction programmes. Approximately 42% of active PWID were HCV-RNA+, with 55 new viraemic infections occurring annually. By 2030, a 60% reduction in the HCV+ PWID population would be expected. In the absence of behavioural changes, the number of secondary infections would increase under all treatment scenarios. With high level treatment, the number of secondary infections would peak and then drop, corresponding to depletion of the viral pool. In Switzerland, 5% treatment of the 2015 HCV+ PWID population per year would result in a 95% reduction in total cases by 2030, whereas ≥10% treatment would result in a >99% reduction. Timely treatment of hepatitis C virus among people who inject drugs is necessary to reduce the prevalence and prevent new infections in Switzerland

Migrating a Well-Established Longitudinal Cohort Database From Oracle SQL to Research Electronic Data Entry (REDCap): Data Management Research and Design Study

BACKGROUND: Providing user-friendly electronic data collection tools for large multicenter studies is key for obtaining high-quality research data. Research Electronic Data Capture (REDCap) is a software solution developed for setting up research databases with integrated graphical user interfaces for electronic data entry. The Swiss Mother and Child HIV Cohort Study (MoCHiV) is a longitudinal cohort study with around 2 million data entries dating back to the early 1980s. Until 2022, data collection in MoCHiV was paper-based. OBJECTIVE: The objective of this study was to provide a user-friendly graphical interface for electronic data entry for physicians and study nurses reporting MoCHiV data. METHODS: MoCHiV collects information on obstetric events among women living with HIV and children born to mothers living with HIV. Until 2022, MoCHiV data were stored in an Oracle SQL relational database. In this project, R and REDCap were used to develop an electronic data entry platform for MoCHiV with migration of already collected data. RESULTS: The key steps for providing an electronic data entry option for MoCHiV were (1) design, (2) data cleaning and formatting, (3) migration and compliance, and (4) add-on features. In the first step, the database structure was defined in REDCap, including the specification of primary and foreign keys, definition of study variables, and the hierarchy of questions (termed "branching logic"). In the second step, data stored in Oracle were cleaned and formatted to adhere to the defined database structure. Systematic data checks ensured compliance to all branching logic and levels of categorical variables. REDCap-specific variables and numbering of repeated events for enabling a relational data structure in REDCap were generated using R. In the third step, data were imported to REDCap and then systematically compared to the original data. In the last step, add-on features, such as data access groups, redirections, and summary reports, were integrated to facilitate data entry in the multicenter MoCHiV study. CONCLUSIONS: By combining different software tools-Oracle SQL, R, and REDCap-and building a systematic pipeline for data cleaning, formatting, and comparing, we were able to migrate a multicenter longitudinal cohort study from Oracle SQL to REDCap. REDCap offers a flexible way for developing customized study designs, even in the case of longitudinal studies with different study arms (ie, obstetric events, women, and mother-child pairs). However, REDCap does not offer built-in tools for preprocessing large data sets before data import. Additional software is needed (eg, R) for data formatting and cleaning to achieve the predefined REDCap data structure

Fitness Ranking of Individual Mutants Drives Patterns of Epistatic Interactions in HIV-1

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Dolutegravir Monotherapy as Maintenance Strategy: A Meta-Analysis of Individual Participant Data From Randomized Controlled Trials

Background Dolutegravir monotherapy (DTG-m) results in virological failure (VF) in some people with human immunodeficiency virus (PWH). We sought to identify the independent factors associated with the risk of VF and to explore the effect size heterogeneity between subgroups of PWH enrolled in DTG-m trials. Methods We searched for randomized clinical trials (RCTs) evaluating DTG-m versus combined antiretroviral therapy (cART) among PWH virologically controlled for at least 6 months on cART. We performed an individual participant data meta-analysis of VF risk factors and quantified their explained heterogeneity in random-effect models. Definition of VF was a confirmed plasma human immunodeficiency virus (HIV)-1 ribonucleic acid (RNA) >50 copies/mL by week 48. Results Among 416 PWH from 4 RCTs, DTG-m significantly increased the risk of VF (16 of 227 [7%] versus 0 of 189 for cART; risk difference 7%; 95% confidence interval [CI], 1%-2%; P = .02; I$^{2}$ = 51%). Among 272 participants exposed to DTG-m, VF were more likely in participants with the following: first cART initiated ≥90 days from HIV acute infection (adjusted hazard ratio [aHR], 5.16; 95% 95% CI, 1.60-16.65), CD4 T cells nadir <350/mm$^{3}$ (aHR, 12.10; 95% CI, 3.92-37.40), HIV RNA signal at baseline (aHR, 4.84; 95% CI, 3.68-6.38), and HIV-deoxyribonucleic acid (DNA) copy number at baseline ≥2.7 log/10$^{6}$ peripheral blood mononuclear cells (aHR, 3.81; 95% CI, 1.99-7.30). Among these independent risk factors, the largest effect size heterogeneity was found between HIV DNA subgroups (I$^{2}$ = 80.2%; P for interaction = .02). Conclusions Our study supports the importance of a large viral reservoir size for explaining DTG-m simplification strategy failure. Further studies are needed to link size and genetic diversity of the HIV-1 reservoir

Predicting the Evolution of Sex on Complex Fitness Landscapes

Most population genetic theories on the evolution of sex or recombination are based on fairly restrictive assumptions about the nature of the underlying fitness landscapes. Here we use computer simulations to study the evolution of sex on fitness landscapes with different degrees of complexity and epistasis. We evaluate predictors of the evolution of sex, which are derived from the conditions established in the population genetic literature for the evolution of sex on simpler fitness landscapes. These predictors are based on quantities such as the variance of Hamming distance, mean fitness, additive genetic variance, and epistasis. We show that for complex fitness landscapes all the predictors generally perform poorly. Interestingly, while the simplest predictor, ΔVarHD, also suffers from a lack of accuracy, it turns out to be the most robust across different types of fitness landscapes. ΔVarHD is based on the change in Hamming distance variance induced by recombination and thus does not require individual fitness measurements. The presence of loci that are not under selection can, however, severely diminish predictor accuracy. Our study thus highlights the difficulty of establishing reliable criteria for the evolution of sex on complex fitness landscapes and illustrates the challenge for both theoretical and experimental research on the origin and maintenance of sexual reproduction

Distributions of epistasis in microbes fit predictions from a fitness landscape model.

How do the fitness effects of several mutations combine? Despite its simplicity, this question is central to the understanding of multilocus evolution. Epistasis (the interaction between alleles at different loci), especially epistasis for fitness traits such as reproduction and survival, influences evolutionary predictions "almost whenever multilocus genetics matters". Yet very few models have sought to predict epistasis, and none has been empirically tested. Here we show that the distribution of epistasis can be predicted from the distribution of single mutation effects, based on a simple fitness landscape model. We show that this prediction closely matches the empirical measures of epistasis that have been obtained for Escherichia coli and the RNA virus vesicular stomatitis virus. Our results suggest that a simple fitness landscape model may be sufficient to quantitatively capture the complex nature of gene interactions. This model may offer a simple and widely applicable alternative to complex metabolic network models, in particular for making evolutionary predictions

Ambiguous Nucleotide Calls From Population-based Sequencing of HIV-1 are a Marker for Viral Diversity and the Age of Infection

The fraction of ambiguous nucleotide calls in bulk sequencing of human immunodeficiency virus type 1 (HIV-1) carries important information on viral diversity and the age of infection. In particular, a fraction of ambiguous nucleotides of >.5% provides evidence against a recent infection event <1 year ago

Genetic Drift of HIV Populations in Culture

Populations of Human Immunodeficiency Virus type 1 (HIV-1) undergo a surprisingly large amount of genetic drift in infected patients despite very large population sizes, which are predicted to be mostly deterministic. Several models have been proposed to explain this phenomenon, but all of them implicitly assume that the process of virus replication itself does not contribute to genetic drift. We developed an assay to measure the amount of genetic drift for HIV populations replicating in cell culture. The assay relies on creation of HIV populations of known size and measurements of variation in frequency of a neutral allele. Using this assay, we show that HIV undergoes approximately ten times more genetic drift than would be expected from its population size, which we defined as the number of infected cells in the culture. We showed that a large portion of the increase in genetic drift is due to non-synchronous infection of target cells. When infections are synchronized, genetic drift for the virus is only 3-fold higher than expected from its population size. Thus, the stochastic nature of biological processes involved in viral replication contributes to increased genetic drift in HIV populations. We propose that appreciation of these effects will allow better understanding of the evolutionary forces acting on HIV in infected patients