Deep reinforcement learning for the direct optimization of gradient separations in liquid chromatography

While Reinforcement Learning (RL) has already proven successful in performing complex tasks, such as controlling large-scale epidemics, mitigating influenza and playing computer games beyond expert level, it is currently largely unexplored in the field of separation sciences. This paper therefore aims to introduce RL, specifically proximal policy optimization (PPO), in liquid chromatography, and evaluate whether it can be trained to optimize separations directly, based solely on the outcome of a single generic separation as input, and a reward signal based on the resolution between peak pairs (taking a value between [-1,1]). More specifically, PPO algorithms or agents were trained to select linear (1-segment) or multi-segment (2-, 3-, or 16-segment) gradients in 1 experiment, based on the outcome of an initial, generic linear gradient (ϕstart=0.3, ϕend=1.0, and tg=20min), to improve separations. The size of the mixtures to be separated varied between 10 and 20 components. Furthermore, two agents, selecting 16-segment gradients, were trained to perform this optimization using either 2 or 3 experiments, in sequence, to investigate whether the agents could improve separations further, based on previous outcomes. Results showed that the PPO agent can improve separations given the outcome of one generic scouting run as input, by selecting ϕ-programs tailored to the mixture under consideration. Allowing agents more freedom in selecting multi-segment gradients increased the reward from 0.891 to 0.908 on average; and allowing the agents to perform an additional experiment increased the reward from 0.908 to 0.918 on average. Finally, the agent outperformed random experiments as well as standard experiments (ϕstart=0.0, ϕend=1.0, and tg=20min) significantly; as random experiments resulted in average rewards between 0.220 and 0.283, and standard experiments resulted in average rewards of 0.840. In conclusion, while there is room for improvement, the results demonstrate the potential of RL in chromatography and present an interesting future direction for the automated optimization of separations.

Serological screening in a large-scale municipal survey in Cascais, Portugal, during the first waves of the COVID-19 pandemic: lessons for future pandemic preparedness efforts

Background

Serological surveys for SARS-CoV-2 were used early in the COVID-19 pandemic to assess epidemiological scenarios. In the municipality of Cascais (Portugal), serological testing combined with a comprehensive socio-demographic, clinical and behavioral questionnaire was offered to residents between May 2020 and beginning of 2021. In this study, we analyze the factors associated with adherence to this municipal initiative, as well as the sociodemographic profile and chronic diseases clinical correlates associated to seropositivity. We aim to contribute with relevant information for future pandemic preparedness efforts.

Methods

This was a cross-sectional study with non-probabilistic sampling. Citizens residing in Cascais Municipality went voluntarily to blood collection centers to participate in the serological survey. The proportion of participants, stratified by socio-demographic variables, was compared to the census proportions to identify the groups with lower levels of adherence to the survey. Univariate and multivariate logistic regression were used to identify socio-demographic, clinical and behavioral factors associated with seropositivity.

Results

From May 2020 to February 2021, 19,608 participants (9.2% of the residents of Cascais) were included in the study. Based on the comparison to census data, groups with lower adherence to this survey were men, the youngest and the oldest age groups, individuals with lower levels of education and unemployed/inactive. Significant predictors of a reactive (positive) serological test were younger age, being employed or a student, and living in larger households. Individuals with chronic diseases generally showed lower seroprevalence.

Conclusion

The groups with low adherence to this voluntary study, as well as the socio-economic contexts identified as more at risk of viral transmission, may be targeted in future pandemic situations. We also found that the individuals with chronic diseases, perceiving higher risk of serious illness, adopted protective behaviors that limited infection rates, revealing that health education on preventive measures was effective for these patients.

Patient-reported outcome measures on mental health and psychosocial factors in patients with Brugada syndrome

AIMS

Brugada syndrome (BrS) is a hereditary arrhythmic disease, associated with sudden cardiac death. To date, little is known about the psychosocial correlates and impacts associated with this disease. The aim of this study was to assess a set of patient-reported psychosocial outcomes, to better profile these patients, and to propose a tailored psychosocial care.

METHODS AND RESULTS

Patients were recruited at the European reference Centre for BrS at Universitair Ziekenhuis Brussel, Belgium. Recruitment was undertaken in two phases: phase 1 (retrospective), patients with confirmed BrS, and phase 2 (prospective), patients referred for ajmaline testing who had an either positive or negative diagnosis. BrS patients were compared to controls from the general population. Two hundred and nine questionnaires were analysed (144 retrospective and 65 prospective). Collected patient-reported outcomes were on mental health (12 item General Health Questionnaire; GHQ-12), social support (Oslo Social Support Scale), health-related quality of life, presence of Type-D personality (Type-D Scale; DS14), coping styles (Brief-COPE), and personality dimensions (Ten Item Personality Inventory). Results showed higher mental distress (GHQ-12) in BrS patients (2.53 ± 3.03) than in the general population (P < 0.001) and higher prevalence (32.7%) of Type D personality (P < 0.001) in patients with confirmed Brugada syndrome (BrS +). A strong correlation was found in the BrS + group (0.611, P < 0.001) between DS14 negative affectivity subscale and mental distress (GHQ-12).

CONCLUSION

Mental distress and type D personality are significantly more common in BrS patients compared to the general population. This clearly illustrates the necessity to include mental health screening and care as standard for BrS.

Pneumococcal vaccination coverage and adherence to recommended dosing schedules in adults: a repeated cross-sectional study of the INTEGO morbidity registry

BACKGROUND

Since 2014, Belgium's Superior Health Council has recommended pneumococcal vaccination for adults aged 19-85 years at increased risk for pneumococcal diseases with a specific vaccine administration sequence and timing. Currently, Belgium has no publicly funded adult pneumococcal vaccination program. This study investigated the seasonal pneumococcal vaccination trends, evolution of vaccination coverage and adherence to the 2014 recommendations.

METHODS

INTEGO is a general practice morbidity registry in Flanders (Belgium) that represents 102 general practice centres and comprised over 300.000 patients in 2021. A repeated cross-sectional study was performed for the period between 2017 and 2021. Using adjusted odds ratios computed via multiple logistic regression, the association between an individual's characteristics (gender, age, comorbidities, influenza vaccination status and socioeconomic status) and schedule-adherent pneumococcal vaccination status was assessed.

RESULTS

Pneumococcal vaccination coincided with seasonal flu vaccination. The vaccination coverage in the population at risk decreased from 21% in 2017 to 18.2% in 2018 and then started to increase to 23.6% in 2021. Coverage in 2021 was highest for high-risk adults (33.8%) followed by 50- to 85-year-olds with comorbidities (25.5%) and healthy 65- to 85-year-olds (18.7%). In 2021, 56.3% of the high-risk adults, 74.6% of the 50+ with comorbidities persons, and 74% of the 65+ healthy persons had an adherent vaccination schedule. Persons with a lower socioeconomic status had an adjusted odds ratio of 0.92 (95% Confidence Interval (CI) 0.87-0.97) for primary vaccination, 0.67 (95% CI 0.60-0.75) for adherence to the recommended second vaccination if the 13-valent pneumococcal conjugate vaccine was administered first and 0.86 (95% CI 0.76-0.97) if the 23-valent pneumococcal polysaccharide vaccine was administered first.

CONCLUSION

Pneumococcal vaccine coverage is slowly increasing in Flanders, displaying seasonal peaks in sync with influenza vaccination campaigns. However, with less than one-fourth of the target population vaccinated, less than 60% high-risk and approximately 74% of 50 + with comorbidities and 65+ healthy persons with an adherent schedule, there is still much room for improvement. Furthermore, adults with poor socioeconomic status had lower odds of primary vaccination and schedule adherence, demonstrating the need for a publicly funded program in Belgium to ensure equitable access.

Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories

Following the onset of the ongoing COVID-19 pandemic throughout the world, a large fraction of the global population is or has been under strict measures of physical distancing and quarantine, with many countries being in partial or full lockdown. These measures are imposed in order to reduce the spread of the disease and to lift the pressure on healthcare systems. Estimating the impact of such interventions as well as monitoring the gradual relaxing of these stringent measures is quintessential to understand how resurgence of the COVID-19 epidemic can be controlled for in the future. In this paper we use a stochastic age-structured discrete time compartmental model to describe the transmission of COVID-19 in Belgium. Our model explicitly accounts for age-structure by integrating data on social contacts to (i) assess the impact of the lockdown as implemented on March 13, 2020 on the number of new hospitalizations in Belgium; (ii) conduct a scenario analysis estimating the impact of possible exit strategies on potential future COVID-19 waves. More specifically, the aforementioned model is fitted to hospital admission data, data on the daily number of COVID-19 deaths and serial serological survey data informing the (sero)prevalence of the disease in the population while relying on a Bayesian MCMC approach. Our age-structured stochastic model describes the observed outbreak data well, both in terms of hospitalizations as well as COVID-19 related deaths in the Belgian population. Despite an extensive exploration of various projections for the future course of the epidemic, based on the impact of adherence to measures of physical distancing and a potential increase in contacts as a result of the relaxation of the stringent lockdown measures, a lot of uncertainty remains about the evolution of the epidemic in the next months.

A data-driven metapopulation model for the Belgian COVID-19 epidemic: assessing the impact of lockdown and exit strategies

BACKGROUND

In response to the ongoing COVID-19 pandemic, several countries adopted measures of social distancing to a different degree. For many countries, after successfully curbing the initial wave, lockdown measures were gradually lifted. In Belgium, such relief started on May 4th with phase 1, followed by several subsequent phases over the next few weeks.

METHODS

We analysed the expected impact of relaxing stringent lockdown measures taken according to the phased Belgian exit strategy. We developed a stochastic, data-informed, meta-population model that accounts for mixing and mobility of the age-structured population of Belgium. The model is calibrated to daily hospitalization data and is able to reproduce the outbreak at the national level. We consider different scenarios for relieving the lockdown, quantified in terms of relative reductions in pre-pandemic social mixing and mobility. We validate our assumptions by making comparisons with social contact data collected during and after the lockdown.

RESULTS

Our model is able to successfully describe the initial wave of COVID-19 in Belgium and identifies interactions during leisure/other activities as pivotal in the exit strategy. Indeed, we find a smaller impact of school re-openings as compared to restarting leisure activities and re-openings of work places. We also assess the impact of case isolation of new (suspected) infections, and find that it allows re-establishing relatively more social interactions while still ensuring epidemic control. Scenarios predicting a second wave of hospitalizations were not observed, suggesting that the per-contact probability of infection has changed with respect to the pre-lockdown period.

CONCLUSIONS

Contacts during leisure activities are found to be most influential, followed by professional contacts and school contacts, respectively, for an impending second wave of COVID-19. Regular re-assessment of social contacts in the population is therefore crucial to adjust to evolving behavioral changes that can affect epidemic diffusion.

On realized serial and generation intervals given control measures: The COVID-19 pandemic case

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.

The generation and serial intervals are epidemiological quantities used to describe and predict an ongoing epidemic outbreak. These quantities are related to the contact pattern of individuals, since infection events can take place if infectious and susceptible individuals have a contact. Therefore, intervention measures that reduce the interactions between members of the population are expected to affect both the realized generation and serial intervals. For the current COVID-19 pandemic unprecedented interventions have been adopted worldwide, e.g. strict lockdown, isolation and quarantine, which influence the realized value of generation and serial intervals. The extent of the effect thereof depends on the efficacy of the control measure in place, on the relationship between symptom onset and infectiousness and on the proportion of infectious individuals that can be detected. To get more insight on this, we present an investigation that highlights the effect of quarantine and isolation on realized generation and serial intervals. In particular, we show that not only their variances but also their mean values can differ, suggesting that the use of the mean serial interval as a proxy for the mean generation time can lead to biased estimates of epidemiological quantities.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Increasing Prevalence of HIV-1 Transmitted Drug Resistance in Portugal: Implications for First Line Treatment Recommendations

INTRODUCTION

Treatment for All recommendations have allowed access to antiretroviral (ARV) treatment for an increasing number of patients. This minimizes the transmission of infection but can potentiate the risk of transmitted (TDR) and acquired drug resistance (ADR).

OBJECTIVE

To study the trends of TDR and ADR in patients followed up in Portuguese hospitals between 2001 and 2017.

METHODS

In total, 11,911 patients of the Portuguese REGA database were included. TDR was defined as the presence of one or more surveillance drug resistance mutation according to the WHO surveillance list. Genotypic resistance to ARV was evaluated with Stanford HIVdb v7.0. Patterns of TDR, ADR and the prevalence of mutations over time were analyzed using logistic regression.

RESULTS AND DISCUSSION

The prevalence of TDR increased from 7.9% in 2003 to 13.1% in 2017 (p < 0.001). This was due to a significant increase in both resistance to nucleotide reverse transcriptase inhibitors (NRTIs) and non-nucleotide reverse transcriptase inhibitors (NNRTIs), from 5.6% to 6.7% (p = 0.002) and 2.9% to 8.9% (p < 0.001), respectively. TDR was associated with infection with subtype B, and with lower viral load levels (p < 0.05). The prevalence of ADR declined from 86.6% in 2001 to 51.0% in 2017 (p < 0.001), caused by decreasing drug resistance to all antiretroviral (ARV) classes (p < 0.001).

CONCLUSIONS

While ADR has been decreasing since 2001, TDR has been increasing, reaching a value of 13.1% by the end of 2017. It is urgently necessary to develop public health programs to monitor the levels and patterns of TDR in newly diagnosed patients.

A prospect on the use of antiviral drugs to control local outbreaks of COVID-19

BACKGROUND

Current outbreaks of COVID-19 are threatening the health care systems of several countries around the world. Control measures, based on isolation, contact tracing, and quarantine, can decrease and delay the burden of the ongoing epidemic. With respect to the ongoing COVID-19 epidemic, recent modeling work shows that these interventions may be inadequate to control local outbreaks, even when perfect isolation is assumed. The effect of infectiousness prior to symptom onset combined with asymptomatic infectees further complicates the use of contact tracing. We aim to study whether antivirals, which decrease the viral load and reduce infectiousness, could be integrated into control measures in order to augment the feasibility of controlling the epidemic.

METHODS

Using a simulation-based model of viral transmission, we tested the efficacy of different intervention measures to control local COVID-19 outbreaks. For individuals that were identified through contact tracing, we evaluate two procedures: monitoring individuals for symptoms onset and testing of individuals. Additionally, we investigate the implementation of an antiviral compound combined with the contact tracing process.

RESULTS

For an infectious disease in which asymptomatic and presymptomatic infections are plausible, an intervention measure based on contact tracing performs better when combined with testing instead of monitoring, provided that the test is able to detect infections during the incubation period. Antiviral drugs, in combination with contact tracing, quarantine, and isolation, result in a significant decrease of the final size and the peak incidence, and increase the probability that the outbreak will fade out.

CONCLUSION

In all tested scenarios, the model highlights the benefits of control measures based on the testing of traced individuals. In addition, the administration of an antiviral drug, together with quarantine, isolation, and contact tracing, is shown to decrease the spread of the epidemic. This control measure could be an effective strategy to control local and re-emerging outbreaks of COVID-19.

Molecular Epidemiology of HIV-1 Infected Migrants Followed up in Portugal: Trends between 2001-2017

Migration is associated with HIV-1 vulnerability. Objectives: To identify long-term trends in HIV-1 molecular epidemiology and antiretroviral drug resistance (ARV) among migrants followed up in Portugal Methods: 5177 patients were included between 2001 and 2017. Rega, Scuel, Comet, and jPHMM algorithms were used for subtyping. Transmitted drug resistance (TDR) and Acquired drug resistance (ADR) were defined as the presence of surveillance drug resistance mutations (SDRMs) and as mutations of the IAS-USA 2015 algorithm, respectively. Statistical analyses were performed. Results: HIV-1 subtypes infecting migrants were consistent with the ones prevailing in their countries of origin. Over time, overall TDR significantly increased and specifically for Non-nucleoside reverse transcriptase inhibitor (NNRTIs) and Nucleoside reverse transcriptase inhibitor (NRTIs). TDR was higher in patients from Mozambique. Country of origin Mozambique and subtype B were independently associated with TDR. Overall, ADR significantly decreased over time and specifically for NRTIs and Protease Inhibitors (PIs). Age, subtype B, and viral load were independently associated with ADR. Conclusions: HIV-1 molecular epidemiology in migrants suggests high levels of connectivity with their country of origin. The increasing levels of TDR in migrants could indicate an increase also in their countries of origin, where more efficient surveillance should occur.

SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination

Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behaviour and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.

PhyloGeoTool: interactively exploring large phylogenies in an epidemiological context

Motivation

Clinicians, health officials and researchers are interested in the epidemic spread of pathogens in both space and time to support the optimization of intervention measures and public health policies. Large sequence databases of virus sequences provide an interesting opportunity to study this spread through phylogenetic analysis. To infer knowledge from large phylogenetic trees, potentially encompassing tens of thousands of virus strains, an efficient method for data exploration is required. The clades that are visited during this exploration should be annotated with strain characteristics (e.g. transmission risk group, tropism, drug resistance profile) and their geographic context.

Results

PhyloGeoTool implements a visual method to explore large phylogenetic trees and to depict characteristics of strains and clades, including their geographic context, in an interactive way. PhyloGeoTool also provides the possibility to position new virus strains relative to the existing phylogenetic tree, allowing users to gain insight in the placement of such new strains without the need to perform a de novo reconstruction of the phylogeny.

Availability and implementation

https://github.com/rega-cev/phylogeotool (Freely available: open source software project).

Supplementary information

Supplementary data are available at Bioinformatics online.

The impact of HIV-1 within-host evolution on transmission dynamics

The adaptive potential of HIV-1 is a vital mechanism to evade host immune responses and antiviral treatment. However, high evolutionary rates during persistent infection can impair transmission efficiency and alter disease progression in the new host, resulting in a delicate trade-off between within-host virulence and between-host infectiousness. This trade-off is visible in the disparity in evolutionary rates at within-host and between-host levels, and preferential transmission of ancestral donor viruses. Understanding the impact of within-host evolution for epidemiological studies is essential for the design of preventive and therapeutic measures. Herein, we review recent theoretical and experimental work that generated new insights into the complex link between within-host evolution and between-host fitness, revealing temporal and selective processes underlying the structure and dynamics of HIV-1 transmission.

Epidemic dispersion of HIV and HCV in a population of co-infected Romanian injecting drug users

Co-infections with HIV and HCV are very frequent among people who inject drugs (PWID). However, very few studies comparatively reconstructed the transmission patterns of both viruses in the same population. We have recruited 117 co-infected PWID during a recent HIV outbreak in Romania. Phylogenetic analyses were performed on HIV and HCV sequences in order to characterize and compare transmission dynamics of the two viruses. Three large HIV clusters (2 subtype F1 and one CRF14_BG) and thirteen smaller HCV transmission networks (genotypes 1a, 1b, 3a, 4a and 4d) were identified. Eighty (65%) patients were both in HIV and HCV transmission chains and 70 of those shared the same HIV and HCV cluster with at least one other patient. Molecular clock analysis indicated that all identified HIV clusters originated around 2006, while the origin of the different HCV clusters ranged between 1980 (genotype 1b) and 2011 (genotypes 3a and 4d). HCV infection preceded HIV infection in 80.3% of cases. Coincidental transmission of HIV and HCV was estimated to be rather low (19.65%) and associated with an outbreak among PWID during detention in the same penitentiary. This study has reconstructed and compared the dispersion of these two viruses in a PWID population.

Exploring resistance pathways for first-generation NS3/4A protease inhibitors boceprevir and telaprevir using Bayesian network learning

Resistance-associated variants (RAVs) have been shown to influence treatment response to direct-acting antivirals (DAAs) and first generation NS3/4A protease inhibitors (PIs) in particular. Interpretation of hepatitis C virus (HCV) genotypic drug resistance remains a challenge, especially in patients who previously failed DAA therapy and need to be retreated with a second DAA based regimen. Bayesian network (BN) learning on HCV sequence data from patients treated with DAAs could provide insight in resistance pathways against PIs for HCV subtypes 1a and 1b, in a similar way as applied before for HIV. The publicly available 'Rega-BN' tool chain was developed to study associative analyses for various pathogens. Our first analysis, comparing sequences from PI-naïve and PI-experienced patients, determined that NS3 substitutions R155K and V36M arise with PI-exposure in HCV1a infected patients, and were defined as major and minor resistance-associated variants respectively. NS3 variant 174H was newly identified as potentially related to PI resistance. In a second analysis, NS3 sequences from PI-naïve patients who cleared the virus during PI therapy and from PI-naïve patients who failed PI therapy were compared, showing that NS3 baseline variant 67S predisposes to treatment-failure and variant 72I to treatment success. This approach has the potential to better characterize the role of more RAVs, if sufficient therapy annotated sequence data becomes available in curated public databases. In addition, polymorphisms present in baseline sequences that predispose patients to therapy failure can be identified using this approach.

Mapping the genomic diversity of HCV subtypes 1a and 1b: Implications of structural and immunological constraints for vaccine and drug development

Despite significant progress in hepatitis C (HCV) treatment, global viral eradication remains a challenge. An in-depth map of its genome diversity within the context of structural and immunological constraints could contribute to the design of pan-genotypic antivirals and preventive vaccines. For such analyses, extensive information is only available for the highly prevalent HCV genotypes (GT) 1a and 1b. Using 647 GT1a and 408 GT1b full-genome sequences obtained from the Los Alamos database, we found that respectively 3 per cent and 82 per cent of all codon positions are under positive and negative selective pressure, suggesting variation mainly accumulates due to random genetic drift. An association between conservation and both structured RNA and secondary protein structures confirmed the important role of structural elements at nucleotide and at amino acid level. Remarkably, CD8⁺ T-cell epitopes in HCV GT1a were significantly more conserved, while at the same time containing more sites under positive selection. Similarly, CD4⁺ T-cell epitopes were significantly more conserved in both HCV subtypes, but under less positive selective pressure in GT1b and more negative selective pressure in GT1a. In contrast, B-cell epitopes in both subtypes were less conserved and under less stringent negative selection. These findings argue against immune selective pressure as the main force of between-host diversifying evolution. Despite its high variability, HCV is under strict evolutionary constraints, most probably to keep its genes and proteins functional during the replication cycle. These are encouraging findings for vaccine and drug design, which could consider these newly established genetic diversity profiles.

HCV1b genome evolution under selective pressure of the cyclophilin inhibitor alisporivir during the DEB-025-HCV-203 phase II clinical trial

Major advances have revolutionized the HCV antiviral treatment field, with interferon-free combinations of direct-acting antivirals (DAAs) resulting into success rates of >90% for all HCV genotypes. Nevertheless, viral eradication at a global level stills remains challenging, stimulating the continued search for new affordable pan-genotypic drugs. To overcome selection of drug resistant variants, targeting host proteins can be an attractive mechanism of action. Alisporivir (Debio 025) is a potent pan-genotypic host-targeting antiviral agent, acting on cyclophilin A, which is necessary for HCV replication. The efficacy and safety of three different oral doses of alisporivir in combination with pegylated interferon-α2a given over a period of four weeks, was investigated in a randomized, double-blind and placebo-controlled phase IIa clinical trial, in 90 treatment-naïve subjects infected with chronic hepatitis C, wherefrom 58 HCV1b samples were selected for genetic sequencing purposes. Sequencing results were used to study the HCV genome for amino acid changes potentially related with selective pressure and resistance to alisporivir. By comparing baseline and on-treatment sequences, a large variation in proportion of amino acid changes was detected in all treatment arms. The NS5A variant D320E, which was previously identified during in vitro resistance selection and resulted in 3.6-fold reduced alisporivir susceptibility, emerged in two subjects in the alisporivir monotherapy arm. However, emergence of D320E appeared to be associated only with concurrent viral load rebound in one subject with 0.8log10IU/ml increase in HCV RNA. In general, for all datasets, low numbers of positions under positive selective pressure were observed, with no significant differences between naïve and treated sequences. Additionally, incomplete sequence information for some of the 22 patients and the low number of individuals per treatment arm, is limiting the power to assess the association of alisporivir or interferon treatment with the observed amino acid changes.

Genetic Diversity and Selective Pressure in Hepatitis C Virus Genotypes 1-6: Significance for Direct-Acting Antiviral Treatment and Drug Resistance

Treatment with pan-genotypic direct-acting antivirals, targeting different viral proteins, is the best option for clearing hepatitis C virus (HCV) infection in chronically infected patients. However, the diversity of the HCV genome is a major obstacle for the development of antiviral drugs, vaccines, and genotyping assays. In this large-scale analysis, genome-wide diversity and selective pressure was mapped, focusing on positions important for treatment, drug resistance, and resistance testing. A dataset of 1415 full-genome sequences, including genotypes 1-6 from the Los Alamos database, was analyzed. In 44% of all full-genome positions, the consensus amino acid was different for at least one genotype. Focusing on positions sharing the same consensus amino acid in all genotypes revealed that only 15% was defined as pan-genotypic highly conserved (≥99% amino acid identity) and an additional 24% as pan-genotypic conserved (≥95%). Despite its large genetic diversity, across all genotypes, codon positions were rarely identified to be positively selected (0.23%-0.46%) and predominantly found to be under negative selective pressure, suggesting mainly neutral evolution. For NS3, NS5A, and NS5B, respectively, 40% (6/15), 33% (3/9), and 14% (2/14) of the resistance-related positions harbored as consensus the amino acid variant related to resistance, potentially impeding treatment. For example, the NS3 variant 80K, conferring resistance to simeprevir used for treatment of HCV1 infected patients, was present in 39.3% of the HCV1a strains and 0.25% of HCV1b strains. Both NS5A variants 28M and 30S, known to be associated with resistance to the pan-genotypic drug daclatasvir, were found in a significant proportion of HCV4 strains (10.7%). NS5B variant 556G, known to confer resistance to non-nucleoside inhibitor dasabuvir, was observed in 8.4% of the HCV1b strains. Given the large HCV genetic diversity, sequencing efforts for resistance testing purposes may need to be genotype-specific or geographically tailored.

Discordant predictions of residual activity could impact dolutegravir prescription upon raltegravir failure

BACKGROUND

Dolutegravir is approved for the treatment of HIV-1 patients exposed to other integrase inhibitors, but the decision to use dolutegravir in this setting should be informed by drug resistance testing.

OBJECTIVES

This study determined the extent of disagreement in predicted residual dolutegravir activity after raltegravir use, and identified individual mutational patterns for which uncertainty exists among HIV-1 expert systems.

STUDY DESIGN

Mutation patterns were classified in raltegravir signature pathways including positions 143, 148 and 155, and interpreted into clinically informative resistance levels using genotypic drug resistance interpretation systems ANRS v24, HIVdb v7.0 and Rega v9.1.0, and instructions of dolutegravir use as approved by the Food and Drug Administration and the European Medicines Agency.

RESULTS

In 216HIV-1 patients failing raltegravir-therapy, 87% patients displayed mutations associated with resistance towards integrase inhibitors. A total of 141 unique mutational patterns were observed, with N155H (25.4%), Q148H (16.2%) and Y143R (8.3%) the most prevalent signature mutations. The Q148 pathway occurred almost exclusively in HIV-1 subtype B viruses. Concordances in predicted dolutegravir susceptibility scores among 5 systems were obtained in 57.8% of patients, and concordant intermediate resistant and concordant resistant scores were only observed in 6.5% and 0.9% of patients, respectively. However, systems individually scored higher levels of dolutegravir intermediate resistance and resistance, ranging from 4.2% to 10.2% and from 14.8% to 22.7% of patients, respectively. A consensus on interpreting the extent of residual activity was lacking in 34.7% of patients and was highly resistance pathway-specific.

CONCLUSIONS

Dolutegravir may potentially be effective in the majority of HIV-1 patients failing raltegravir, but concern over the uncertainty in predicted residual activity could withhold clinicians from prescribing dolutegravir during its clinical assessment.

Automated subtyping of HIV-1 genetic sequences for clinical and surveillance purposes: performance evaluation of the new REGA version 3 and seven other tools

BACKGROUND

To investigate differences in pathogenesis, diagnosis and resistance pathways between HIV-1 subtypes, an accurate subtyping tool for large datasets is needed. We aimed to evaluate the performance of automated subtyping tools to classify the different subtypes and circulating recombinant forms using pol, the most sequenced region in clinical practice. We also present the upgraded version 3 of the Rega HIV subtyping tool (REGAv3).

METHODOLOGY

HIV-1 pol sequences (PR+RT) for 4674 patients retrieved from the Portuguese HIV Drug Resistance Database, and 1872 pol sequences trimmed from full-length genomes retrieved from the Los Alamos database were classified with statistical-based tools such as COMET, jpHMM and STAR; similarity-based tools such as NCBI and Stanford; and phylogenetic-based tools such as REGA version 2 (REGAv2), REGAv3, and SCUEAL. The performance of these tools, for pol, and for PR and RT separately, was compared in terms of reproducibility, sensitivity and specificity with respect to the gold standard which was manual phylogenetic analysis of the pol region.

RESULTS

The sensitivity and specificity for subtypes B and C was more than 96% for seven tools, but was variable for other subtypes such as A, D, F and G. With regard to the most common circulating recombinant forms (CRFs), the sensitivity and specificity for CRF01_AE was ~99% with statistical-based tools, with phylogenetic-based tools and with Stanford, one of the similarity based tools. CRF02_AG was correctly identified for more than 96% by COMET, REGAv3, Stanford and STAR. All the tools reached a specificity of more than 97% for most of the subtypes and the two main CRFs (CRF01_AE and CRF02_AG). Other CRFs were identified only by COMET, REGAv2, REGAv3, and SCUEAL and with variable sensitivity. When analyzing sequences for PR and RT separately, the performance for PR was generally lower and variable between the tools. Similarity and statistical-based tools were 100% reproducible, but this was lower for phylogenetic-based tools such as REGA (~99%) and SCUEAL (~96%).

CONCLUSIONS

REGAv3 had an improved performance for subtype B and CRF02_AG compared to REGAv2 and is now able to also identify all epidemiologically relevant CRFs. In general the best performing tools, in alphabetical order, were COMET, jpHMM, REGAv3, and SCUEAL when analyzing pure subtypes in the pol region, and COMET and REGAv3 when analyzing most of the CRFs. Based on this study, we recommend to confirm subtyping with 2 well performing tools, and be cautious with the interpretation of short sequences.

RegaDB: community-driven data management and analysis for infectious diseases

Summary: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface.

Availability and implementation: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.

Contact:pieter.libin@rega.kuleuven.be

Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response

Introduction

Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period.

Materials & Methods

9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test.

Results

Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76–1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69–1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80–1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly.

Conclusion

Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.

Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients

BACKGROUND

The effect of drug resistance transmission on disease progression in the newly infected patient is not well understood. Major drug resistance mutations severely impair viral fitness in a drug free environment, and therefore are expected to revert quickly. Compensatory mutations, often already polymorphic in wild-type viruses, do not tend to revert after transmission. While compensatory mutations increase fitness during treatment, their presence may also modulate viral fitness and virulence in absence of therapy and major resistance mutations. We previously designed a modeling technique that quantifies genotypic footprints of in vivo treatment selective pressure, including both drug resistance mutations and polymorphic compensatory mutations, through the quantitative description of a fitness landscape from virus genetic sequences.

RESULTS

Genotypic correlates of viral load and CD4 cell count were evaluated in subtype B sequences from recently diagnosed treatment-naive patients enrolled in the SPREAD programme. The association of surveillance drug resistance mutations, reported compensatory mutations and fitness estimated from drug selective pressure fitness landscapes with baseline viral load and CD4 cell count was evaluated using regression techniques. Protease genotypic variability estimated to increase fitness during treatment was associated with higher viral load and lower CD4 cell counts also in treatment-naive patients, which could primarily be attributed to well-known compensatory mutations at highly polymorphic positions. By contrast, treatment-related mutations in reverse transcriptase could not explain viral load or CD4 cell count variability.

CONCLUSIONS

These results suggest that polymorphic compensatory mutations in protease, reported to be selected during treatment, may improve the replicative capacity of HIV-1 even in absence of drug selective pressure or major resistance mutations. The presence of this polymorphic variation may either reflect a history of drug selective pressure, i.e. transmission from a treated patient, or merely be a result of diversity in wild-type virus. Our findings suggest that transmitted drug resistance has the potential to contribute to faster disease progression in the newly infected host and to shape the HIV-1 epidemic at a population level.

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine

A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, an in vivo fitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly with in vitro resistance phenotype in genotype-phenotype pairs (R(2)=0.70). Variation in predicted evolution under selective pressure correlated significantly with observed in vivo evolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

A standardized framework for accurate, high-throughput genotyping of recombinant and non-recombinant viral sequences

Human immunodeficiency virus type-1 (HIV-1), hepatitis B and C and other rapidly evolving viruses are characterized by extremely high levels of genetic diversity. To facilitate diagnosis and the development of prevention and treatment strategies that efficiently target the diversity of these viruses, and other pathogens such as human T-lymphotropic virus type-1 (HTLV-1), human herpes virus type-8 (HHV8) and human papillomavirus (HPV), we developed a rapid high-throughput-genotyping system. The method involves the alignment of a query sequence with a carefully selected set of pre-defined reference strains, followed by phylogenetic analysis of multiple overlapping segments of the alignment using a sliding window. Each segment of the query sequence is assigned the genotype and sub-genotype of the reference strain with the highest bootstrap (>70%) and bootscanning (>90%) scores. Results from all windows are combined and displayed graphically using color-coded genotypes. The new Virus-Genotyping Tools provide accurate classification of recombinant and non-recombinant viruses and are currently being assessed for their diagnostic utility. They have incorporated into several HIV drug resistance algorithms including the Stanford (http://hivdb.stanford.edu) and two European databases (http://www.umcutrecht.nl/subsite/spread-programme/ and http://www.hivrdb.org.uk/) and have been successfully used to genotype a large number of sequences in these and other databases. The tools are a PHP/JAVA web application and are freely accessible on a number of servers including: http://bioafrica.mrc.ac.za/rega-genotype/html/, http://lasp.cpqgm.fiocruz.br/virus-genotype/html/, http://jose.med.kuleuven.be/genotypetool/html/.

A collaborative environment allowing clinical investigations on integrated biomedical databases

In order to perform clinical investigations on integrated biomedical data sets and to predict virological and epidemiological outcome, medical experts require an IT-based collaborative environment that provides them a user-friendly space for building and executing their complex studies and workflows on largely available and high-quality data repositories. In this paper, the authors introduce such a novel collaborative working environment a so-called virtual laboratory for clinicians and medical researchers, which allows users to interactively access and browse several biomedical research databases and re-use relevant data sets within own designed experiments. Firstly, technical details on the integration of relevant data resources into the virtual laboratory infrastructure and specifically developed user interfaces are briefly explained. The second part describes research possibilities for medical scientists including potential application fields and benefits as using the virtual laboratory functionalities for a particular exemplary study.

The rise and fall of K65R in a Portuguese HIV-1 Drug Resistance database, despite continuously increasing use of tenofovir

OBJECTIVE

The overall prevalence of the K65R mutation in HIV-1 reverse transcriptase has increased in treatment-experienced patients, mostly attributed to the increasing use of tenofovir (TDF). A number of TDF-based regimens are associated with high rate of early virological failure. In this study, we evaluated the impact of these combinations on K65R selection over time.

METHODS

Treatment-experienced patients who had a genotypic resistance test at time of failure in the period 2002-2005 in a hospital in Lisbon were included. Incidence of K65R was calculated and compared with proportions of failing therapies in the respective year of sampling including TDF or didanosine plus stavudine and their respective frequency of K65R selection were analyzed using classical statistics and Bayesian Network Learning.

RESULTS

The overall rate of K65R in the database was consistent with earlier reports. The incidence of K65R increased in the period 2002-2004 but showed a sharp, significant decrease in 2005, despite a continuous increase of patients failing TDF-based regimens. The proportion of patients failing certain not-recommended regimens decreased sharply in 2005 and therefore correlated well with K65R incidence trend.

CONCLUSIONS

This study suggests that the use of certain TDF-based regimens caused the increase in K65R incidence over the last years.